{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "c979f719-a7bd-4392-86f9-f699220b2c93",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>STATEFP20</th>\n",
       "      <th>COUNTYFP20</th>\n",
       "      <th>TRACTCE20</th>\n",
       "      <th>GEOID20</th>\n",
       "      <th>NAME20</th>\n",
       "      <th>NAMELSAD20</th>\n",
       "      <th>MTFCC20</th>\n",
       "      <th>FUNCSTAT20</th>\n",
       "      <th>ALAND20</th>\n",
       "      <th>AWATER20</th>\n",
       "      <th>...</th>\n",
       "      <th>P0050002</th>\n",
       "      <th>P0050003</th>\n",
       "      <th>P0050004</th>\n",
       "      <th>P0050005</th>\n",
       "      <th>P0050006</th>\n",
       "      <th>P0050007</th>\n",
       "      <th>P0050008</th>\n",
       "      <th>P0050009</th>\n",
       "      <th>P0050010</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>04</td>\n",
       "      <td>015</td>\n",
       "      <td>950500</td>\n",
       "      <td>04015950500</td>\n",
       "      <td>9505</td>\n",
       "      <td>Census Tract</td>\n",
       "      <td>G5020</td>\n",
       "      <td>S</td>\n",
       "      <td>1255016476</td>\n",
       "      <td>39469850</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>POLYGON ((-114.67888 35.50137, -114.67883 35.5...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>04</td>\n",
       "      <td>015</td>\n",
       "      <td>954900</td>\n",
       "      <td>04015954900</td>\n",
       "      <td>9549</td>\n",
       "      <td>Census Tract</td>\n",
       "      <td>G5020</td>\n",
       "      <td>S</td>\n",
       "      <td>22392503</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>106</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>97</td>\n",
       "      <td>9</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "      <td>POLYGON ((-114.10394 35.26982, -114.10256 35.2...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>04</td>\n",
       "      <td>015</td>\n",
       "      <td>951800</td>\n",
       "      <td>04015951800</td>\n",
       "      <td>9518</td>\n",
       "      <td>Census Tract</td>\n",
       "      <td>G5020</td>\n",
       "      <td>S</td>\n",
       "      <td>3648437</td>\n",
       "      <td>120729</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>19</td>\n",
       "      <td>POLYGON ((-114.64029 35.09852, -114.64012 35.0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>04</td>\n",
       "      <td>015</td>\n",
       "      <td>952400</td>\n",
       "      <td>04015952400</td>\n",
       "      <td>9524</td>\n",
       "      <td>Census Tract</td>\n",
       "      <td>G5020</td>\n",
       "      <td>S</td>\n",
       "      <td>295669288</td>\n",
       "      <td>7885448</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>POLYGON ((-114.48778 34.71722, -114.48622 34.7...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>04</td>\n",
       "      <td>027</td>\n",
       "      <td>012100</td>\n",
       "      <td>04027012100</td>\n",
       "      <td>121</td>\n",
       "      <td>Census Tract</td>\n",
       "      <td>G5020</td>\n",
       "      <td>S</td>\n",
       "      <td>6508727795</td>\n",
       "      <td>60851</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>POLYGON ((-114.47325 33.02788, -114.45989 33.0...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 345 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  STATEFP20 COUNTYFP20 TRACTCE20      GEOID20 NAME20    NAMELSAD20 MTFCC20  \\\n",
       "0        04        015    950500  04015950500   9505  Census Tract   G5020   \n",
       "1        04        015    954900  04015954900   9549  Census Tract   G5020   \n",
       "2        04        015    951800  04015951800   9518  Census Tract   G5020   \n",
       "3        04        015    952400  04015952400   9524  Census Tract   G5020   \n",
       "4        04        027    012100  04027012100    121  Census Tract   G5020   \n",
       "\n",
       "  FUNCSTAT20     ALAND20  AWATER20  ... P0050002 P0050003 P0050004 P0050005  \\\n",
       "0          S  1255016476  39469850  ...        0        0        0        0   \n",
       "1          S    22392503         0  ...      106        0        0       97   \n",
       "2          S     3648437    120729  ...        0        0        0        0   \n",
       "3          S   295669288   7885448  ...        0        0        0        0   \n",
       "4          S  6508727795     60851  ...        0        0        0        0   \n",
       "\n",
       "  P0050006 P0050007 P0050008 P0050009 P0050010  \\\n",
       "0        0        0        0        0        0   \n",
       "1        9       22        0        0       22   \n",
       "2        0       19        0        0       19   \n",
       "3        0       15        0        0       15   \n",
       "4        0       11        0       11        0   \n",
       "\n",
       "                                            geometry  \n",
       "0  POLYGON ((-114.67888 35.50137, -114.67883 35.5...  \n",
       "1  POLYGON ((-114.10394 35.26982, -114.10256 35.2...  \n",
       "2  POLYGON ((-114.64029 35.09852, -114.64012 35.0...  \n",
       "3  POLYGON ((-114.48778 34.71722, -114.48622 34.7...  \n",
       "4  POLYGON ((-114.47325 33.02788, -114.45989 33.0...  \n",
       "\n",
       "[5 rows x 345 columns]"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# this is for working with census tracts and VTD precincts\n",
    "from shapely.geometry import Point, LineString, Polygon\n",
    "import shapely\n",
    "import geopandas as gpd\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from numpy import random\n",
    "from scipy.stats import norm\n",
    "import math\n",
    "tractPopFile = gpd.read_file(\"state_map_files/az_pl2020_t.dbf\") #for Texas, need only this file\n",
    "tractPopFile.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "208d6842-f420-4205-859d-888e06316d7a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#handy function for plotting Polygon or multiPolygon tracts and precincts\n",
    "def plotPoly(inputPoly):\n",
    "    simplePoly = Polygon([(0,0),(0,1),(1,1)])\n",
    "    if inputPoly.type == simplePoly.type:\n",
    "        x,y = inputPoly.exterior.xy\n",
    "        plt.plot(x,y)\n",
    "    else:\n",
    "        for geom in inputPoly.geoms:\n",
    "            x,y = geom.exterior.xy\n",
    "            plt.plot(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "a7a90b04-4b48-4ab8-9195-8d953907c3fb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "there are 1765 popn tracts for AZ\n"
     ]
    }
   ],
   "source": [
    "# EXTRACT TRACT GEOMETRIES AND POPULATIONS INTO LISTS, COMPUTE TRACT AREAS\n",
    "# If the population and geometry data are in one file, this should work.\n",
    "STATE = \"AZ\"\n",
    "tractGeom = tractPopFile['geometry']  #for some states, replace with tractGeomFile\n",
    "tractPop = tractPopFile['P0010001']\n",
    "tractVAP = tractPopFile['P0030001']   #NEW 3/2/22 - USE VAP\n",
    "# tractPop2 = tractPopFile['P0020001']   #not needed; confirmed that this matches P00100001 exactly\n",
    "tractHisp = tractPopFile['P0040002']   #NEW 3/2/22 - USE VAP\n",
    "tractBlack = tractPopFile['P0030004']  #NEW 3/2/22 - USE VAP\n",
    "nTracts = len(tractPop)\n",
    "print(\"there are {0} popn tracts for {1}\".format(nTracts, STATE) )\n",
    "tractArea = [0.]*nTracts\n",
    "for t in range (0,nTracts) :\n",
    "    tractArea[t] = tractGeom[t].area\n",
    "isSkippedTract = [0] *nTracts  #this will house a temporary list of tracts for manipulation\n",
    "tractPop = tractPop.to_numpy()  #to avoid panda overwrite grousing\n",
    "tractBlack= tractBlack.to_numpy()\n",
    "tractHisp = tractHisp.to_numpy()\n",
    "tractVAP = tractVAP.to_numpy()\n",
    "stateVAP = np.sum(tractVAP)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c90b3908-e2e3-46b8-98e5-ae285eca1ee3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is Hispanic population by total population AZ voter-age population\n",
      "state pop= 7151502 VAP pct Hispanic is  0.26878806404069594\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#What is our correlation of Hispanic pop to total pop?\n",
    "print(\"this is Hispanic population by total population \"+STATE,\"voter-age population\")\n",
    "print(\"state pop=\",np.sum(tractPop), \"VAP pct Hispanic is \",np.sum(tractHisp)/np.sum(tractVAP) )\n",
    "fig, ax = plt.subplots()\n",
    "ax.set(xlabel=\"tract Voter Age Population\", ylabel=\"tract Hispanic pop\")\n",
    "x = [0,10000]\n",
    "y = [0,10000]\n",
    "plt.plot(tractVAP, tractHisp, marker='.',linestyle=\"none\")\n",
    "plt.plot(x,y,linestyle = 'dashed')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "237df16d-af87-47ba-acb1-2ef5702c1406",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is voter-age Black population by total VAP AZ\n",
      "total state pop= 7151502 , VAP pct Black is  0.044683159941508226\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#What is our correlation of Black pop to total pop?\n",
    "print(\"this is voter-age Black population by total VAP \"+STATE)\n",
    "print(\"total state pop=\",np.sum(tractPop), \", VAP pct Black is \",np.sum(tractBlack)/np.sum(tractVAP) )\n",
    "fig, ax = plt.subplots()\n",
    "ax.set(xlabel=\"tract VAP\", ylabel=\"tract Black VAP\")\n",
    "x = [0,10000]\n",
    "y = [0,10000]\n",
    "plt.plot(tractVAP, tractBlack, marker='.',linestyle=\"none\")\n",
    "plt.plot(x,y,linestyle = 'dashed')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "10b208af-8eb0-45fa-9dc0-8873eb797200",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is a histogram of Census tract population for AZ\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#for curiosity, let's plot the distribution of populations among tracts\n",
    "n_bins=50\n",
    "print(\"this is a histogram of Census tract population for \"+STATE)        \n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(tractPop, bins=n_bins)\n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4aac60d6-4cce-469b-b075-ad743acda38c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is population by Census tract for AZ\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#What is our distribution of tract populations by area?\n",
    "print(\"this is population by Census tract for \"+STATE)  \n",
    "plt.plot(tractPop, tractArea, marker='.',linestyle=\"none\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f159bf55-a25e-45c0-b7ea-4c21fbce9bb0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'shapely.geometry.polygon.Polygon'> <class 'shapely.geometry.polygon.Polygon'>\n"
     ]
    }
   ],
   "source": [
    "print( type(tractGeom[0]),type(tractGeom[1]) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "5e8f8f92-a336-43e5-8ac5-20c657cb5857",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "looking for nonPolygons in census tract data\n"
     ]
    }
   ],
   "source": [
    "print(\"looking for nonPolygons in census tract data\")\n",
    "notPoly = [0]*nTracts\n",
    "for m in range(nTracts):\n",
    "    if type(tractGeom[m]) != type(tractGeom[1]):  #assuming tract 1 is a single polygon\n",
    "        notPoly[m] = 1\n",
    "        print(m,tractPop[m], tractArea[m], \"nonPolygon tract no, pop, area\")\n",
    "        x = tractGeom[m].centroid.x\n",
    "        y = tractGeom[m].centroid.y\n",
    "        if y < 999 :  #let's zoom in on these\n",
    "            for geom in tractGeom[m].geoms :\n",
    "                xg,yg = geom.exterior.xy\n",
    "                print(geom.area)\n",
    "                plt.plot(xg,yg)\n",
    "            plt.text(x+0.0,y+0.01,m,fontsize=9)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d34b5fe5-6f02-413a-8c7b-c6662fa756ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#convex-hull these right away  (decision for each state - select ones for WA)\n",
    "tractFixList = [1056,1399]\n",
    "for tFL in range(len(tractFixList)):\n",
    "    m = tractFixList[tFL]\n",
    "\n",
    "#for m in range(nTracts):\n",
    "    if notPoly[m] == 1:\n",
    "        for geom in tractGeom[m].geoms :\n",
    "            xg,yg = geom.exterior.xy\n",
    "            plt.plot(xg,yg)\n",
    "        tractGeom[m] = tractGeom[m].convex_hull\n",
    "        x,y = tractGeom[m].exterior.xy\n",
    "        plt.plot(x,y)\n",
    "        notPoly[m] = 0\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a05c0ec2-d208-45b8-8ff6-c1f1f583ad5d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[11, 22, 265, 346, 432, 736, 976, 1012, 1016, 1132, 1180, 1246, 1322]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# For potential later issues, identify and flag lakeshore low-population tracts.  #skip for Arizona\n",
    "lakeTracts = [-99999]\n",
    "minTractPop = 50\n",
    "for m in range(nTracts):\n",
    "    if tractPop[m] < minTractPop:\n",
    "        x = tractGeom[m].centroid.x\n",
    "        y = tractGeom[m].centroid.y\n",
    "        x2,y2 = tractGeom[m].exterior.xy  #turn these two on to show state map\n",
    "        if y < 48 and x > -96 : #leftover from another state\n",
    "            plt.plot(x2,y2,c=\"purple\")\n",
    "            plt.text(x, y,m, fontsize=9)\n",
    "        #plt.scatter(x,y)\n",
    "        if y > 40: #don't worry about Atlantic ocean tracts\n",
    "            if m==761 or m ==676 or m==825: #trust me on this for Wisconsin\n",
    "                    thisTract = \"interior\"\n",
    "            else:\n",
    "                if lakeTracts == [-99999]:\n",
    "                    lakeTracts = [m]\n",
    "                    isSkippedTract[m] = 1\n",
    "                else:\n",
    "                    lakeTracts.append(m)\n",
    "                    isSkippedTract[m] = 1\n",
    "\n",
    "print(lakeTracts)  # assigned as skipped tracts as well\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "da1f99a3-04d0-427b-95e3-d9a57836959d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "oceanTracts = [1094,1690,1239,607,785]\n",
    "for t in oceanTracts:\n",
    "    x,y = tractGeom[t].exterior.xy\n",
    "    plt.plot(x,y)\n",
    "    isSkippedTract[t] = 1\n",
    "                \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "b0e9e246-a558-475c-8181-075a9421034e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1765 0\n"
     ]
    }
   ],
   "source": [
    "print(len(isSkippedTract),np.sum(isSkippedTract))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d63968a2-afc1-4913-863c-4c415fb9c0b8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "looking for tracts with zero area\n"
     ]
    }
   ],
   "source": [
    "# For potential later issues, identify and visualize zero-Area tracts.  See Ohio code for more code\n",
    "#isSkippedTract = [0]*nTracts\n",
    "print(\"looking for tracts with zero area\")\n",
    "for m in range(nTracts):\n",
    "    if(tractArea[m] == 0):       \n",
    "        x = tractGeom[m].centroid.x\n",
    "        y = tractGeom[m].centroid.y\n",
    "        print( m,tractPop[m],\"(\",x,\",\",y,\")\" )\n",
    "        x2,y2 = tractGeom[m].exterior.xy\n",
    "        plt.plot(x2,y2,c=\"purple\")\n",
    "        #print(tractGeom[m])\n",
    "        plt.scatter(x, y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "baabb633-83ac-4315-92bd-1d6fdd9e999b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Now, manually flag tracts that should be excluded from the map, such as lakeshore empty tracts\n",
    "isSkippedTract = [0]*nTracts\n",
    "#skipList = [1108,2676]   #Lake Erie and northeasternmost Susquehanna.  All others are small.\n",
    "for s in skipList:\n",
    "    isSkippedTract[s] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "72abde7f-085d-4c0a-abe3-29e8d6203b40",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CDE_COUNTY</th>\n",
       "      <th>PCTNUM</th>\n",
       "      <th>PRECINCTNA</th>\n",
       "      <th>G20PREDBID</th>\n",
       "      <th>G20PRERTRU</th>\n",
       "      <th>G20PRELJOR</th>\n",
       "      <th>G20USSDKEL</th>\n",
       "      <th>G20USSRMCS</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AP</td>\n",
       "      <td>AP0002</td>\n",
       "      <td>ALPINE</td>\n",
       "      <td>94</td>\n",
       "      <td>283</td>\n",
       "      <td>5</td>\n",
       "      <td>98</td>\n",
       "      <td>284</td>\n",
       "      <td>POLYGON ((-109.49567 33.65280, -109.49576 33.6...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AP</td>\n",
       "      <td>AP0003</td>\n",
       "      <td>CANYON DE CHELLY</td>\n",
       "      <td>1982</td>\n",
       "      <td>273</td>\n",
       "      <td>30</td>\n",
       "      <td>1995</td>\n",
       "      <td>290</td>\n",
       "      <td>POLYGON ((-109.71666 36.26151, -109.71658 36.2...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AP</td>\n",
       "      <td>AP0005</td>\n",
       "      <td>CHINLE</td>\n",
       "      <td>989</td>\n",
       "      <td>148</td>\n",
       "      <td>20</td>\n",
       "      <td>994</td>\n",
       "      <td>157</td>\n",
       "      <td>POLYGON ((-109.81183 36.27512, -109.80810 36.2...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AP</td>\n",
       "      <td>AP0009</td>\n",
       "      <td>CONCHO</td>\n",
       "      <td>333</td>\n",
       "      <td>1486</td>\n",
       "      <td>22</td>\n",
       "      <td>381</td>\n",
       "      <td>1422</td>\n",
       "      <td>POLYGON ((-109.53982 34.44871, -109.53928 34.4...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AP</td>\n",
       "      <td>AP0011</td>\n",
       "      <td>COTTONWOOD</td>\n",
       "      <td>748</td>\n",
       "      <td>87</td>\n",
       "      <td>5</td>\n",
       "      <td>763</td>\n",
       "      <td>85</td>\n",
       "      <td>POLYGON ((-109.81768 36.14760, -109.81822 36.1...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  CDE_COUNTY  PCTNUM        PRECINCTNA  G20PREDBID  G20PRERTRU  G20PRELJOR  \\\n",
       "0         AP  AP0002            ALPINE          94         283           5   \n",
       "1         AP  AP0003  CANYON DE CHELLY        1982         273          30   \n",
       "2         AP  AP0005            CHINLE         989         148          20   \n",
       "3         AP  AP0009            CONCHO         333        1486          22   \n",
       "4         AP  AP0011        COTTONWOOD         748          87           5   \n",
       "\n",
       "   G20USSDKEL  G20USSRMCS                                           geometry  \n",
       "0          98         284  POLYGON ((-109.49567 33.65280, -109.49576 33.6...  \n",
       "1        1995         290  POLYGON ((-109.71666 36.26151, -109.71658 36.2...  \n",
       "2         994         157  POLYGON ((-109.81183 36.27512, -109.80810 36.2...  \n",
       "3         381        1422  POLYGON ((-109.53982 34.44871, -109.53928 34.4...  \n",
       "4         763          85  POLYGON ((-109.81768 36.14760, -109.81822 36.1...  "
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Now, let's read in the voting data  \n",
    "VTDdbf = gpd.read_file(\"state_map_files/az_vest_20.dbf\")\n",
    "VTDdbf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "4f3b9ddb-b971-472c-a353-521377f95e46",
   "metadata": {},
   "outputs": [],
   "source": [
    "#VTDdbf = VTDdbf.to_crs(tractPopFile.crs)  #MD's precinct file in wrong CRS\n",
    "#VTDdbf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "3fe4ceba-8077-4485-ad0a-f8f2a5755040",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1489 0.49843168320870684 3333829 = number of precincts, statewide GOP vote, total Trump+Biden votes\n"
     ]
    }
   ],
   "source": [
    "# Pull VTD geopandas data columns into arrays\n",
    "vtdGeom = VTDdbf['geometry'] \n",
    "# vtdGeom = VTDdbf['geometry'] #can't use VTDdbf because it uses alternate coordinate system\n",
    "vtdTrump = VTDdbf['G20PRERTRU']\n",
    "vtdBiden = VTDdbf['G20PREDBID']\n",
    "\n",
    "nPrecincts = len(vtdGeom)\n",
    "stateGOP = np.sum(vtdTrump)/(np.sum(vtdTrump) + np.sum(vtdBiden) ) \n",
    "print(nPrecincts, stateGOP, np.sum(vtdTrump) + np.sum(vtdBiden),\n",
    "      \"= number of precincts, statewide GOP vote, total Trump+Biden votes\" )\n",
    "vtdTrump = vtdTrump.to_numpy()  #these two lines try to avoid pandas complaints when we overwrite precinct data\n",
    "vtdBiden = vtdBiden.to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "c5c57f52-b8d2-4ef4-9d02-2429ed5e59da",
   "metadata": {},
   "outputs": [],
   "source": [
    "#ID the non-polygon PRECINCTS\n",
    "notPolyVTD = [0]*nPrecincts\n",
    "for p in range(nPrecincts):\n",
    "    if type(vtdGeom[p]) != type(vtdGeom[0]):\n",
    "        notPolyVTD[p] = 1\n",
    "        for geom in vtdGeom[p].geoms:    \n",
    "            xs, ys = geom.exterior.xy\n",
    "            plt.plot(xs,ys)\n",
    "        #plt.text(vtdGeom[p].centroid.x, vtdGeom[p].centroid.y,p)\n",
    "    else:\n",
    "        hi = \"hi\"\n",
    "        #x,y = vtdGeom[p].exterior.xy\n",
    "        #plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "ea498c6a-d054-409e-92fb-cda921e2911f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#prep for geography triage\n",
    "#isSkippedTract = [0]*nTracts  #done above; already skipped two lakeshore tracts\n",
    "isSkippedPrecinct = [0]*nPrecincts  #will be used later"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "3ded1b1f-5629-4d8f-826b-e037e5dcb3da",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on tract 200\n",
      "working on tract 400\n",
      "working on tract 600\n",
      "working on tract 800\n",
      "working on tract 1000\n",
      "working on tract 1200\n",
      "working on tract 1400\n",
      "working on tract 1600\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#build basic state map\n",
    "#tractMAP = tractGeom[0] #uncomment for first time thru loop  **************\n",
    "for t in range(1,nTracts):\n",
    "    if t%200 == 0:\n",
    "        print(\"working on tract\",t)\n",
    "    if isSkippedTract[t] == 0:\n",
    "        uncomment = \"if-redone\"\n",
    "        #tractMAP = tractMAP.union(tractGeom[t])  #uncomment to build map  ********\n",
    "plotPoly(tractMAP)\n",
    "\n",
    "pt1 = Point(-114.9,32)\n",
    "pt2 = Point(-114.5,32.2)\n",
    "pt3 = Point(-114.45,32.8)\n",
    "pt4 = Point(-114.9,32.8)\n",
    "clipPoly = Polygon([pt1,pt2,pt3,pt4])  #clip eastern outcropping NE of Philly\n",
    "plotPoly(clipPoly)\n",
    "\n",
    "plt.show()\n",
    "wholeMAP = tractMAP  #in case we later slice parts out\n",
    "            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "f33de347-5473-4b11-b3ae-86691b33098d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I have flagged  47 tracts w pop 170446.0  to reassign to tracts...\n",
      "[4, 12, 15, 238, 479, 990, 1021, 1431, 1529, 1531, 1534, 1536, 1540, 1541, 1542]\n",
      "I have flagged  36 precincts to reassign to precincts...\n",
      "[101, 221, 222, 224, 348, 454, 456, 476, 481]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#find all tracts and precincts centered in this clipPoly, flag for cutting\n",
    "# and, ID all nearby tracts and precincts to receive their pops and voters\n",
    "\n",
    "cutTractList = [-99999]\n",
    "cutPrecinctList = [-99999]\n",
    "tractReceivers = [-88888]\n",
    "precinctReceivers = [-88888]\n",
    "popToCut = 0.\n",
    "for t in range(nTracts):\n",
    "    x = tractGeom[t].centroid.x\n",
    "    y = tractGeom[t].centroid.y\n",
    "    if tractGeom[t].intersects(clipPoly) :\n",
    "        CP = Point(x,y)\n",
    "        if CP.intersects(clipPoly):\n",
    "            isSkippedTract[t] = 1  #uncomment once confirmed\n",
    "            popToCut += tractPop[t]\n",
    "            if cutTractList == [-99999]:\n",
    "                cutTractList = [t]\n",
    "            else:\n",
    "                cutTractList.append(t)\n",
    "            plotPoly(tractGeom[t])\n",
    "    else:\n",
    "        if tractGeom[t].distance(clipPoly) < 0.5 : #and tractGeom[t].centroid.y < 33.2 :\n",
    "            if tractReceivers == [-88888]:\n",
    "                tractReceivers = [t]\n",
    "            else:\n",
    "                tractReceivers.append(t)\n",
    "xp,yp = clipPoly.exterior.xy\n",
    "plt.plot(xp,yp)\n",
    "\n",
    "for p in range(nPrecincts):\n",
    "    x = vtdGeom[p].centroid.x\n",
    "    y = vtdGeom[p].centroid.y\n",
    "    if vtdGeom[p].intersects(clipPoly) and Point(x,y).intersects(clipPoly) :   #for Arizona, centroid\n",
    "        \n",
    "        isSkippedPrecinct[p] = 1  #uncomment once confirmed\n",
    "        if cutPrecinctList == [-99999]:\n",
    "            cutPrecinctList = [p]\n",
    "        else:\n",
    "            cutPrecinctList.append(p)\n",
    "    else:\n",
    "        if vtdGeom[p].distance(clipPoly) < 0.5 : # and vtdGeom[p].centroid.y < 33.2 :\n",
    "            if precinctReceivers == [-88888]:\n",
    "                precinctReceivers = [p]\n",
    "            else:\n",
    "                precinctReceivers.append(p)\n",
    "print(\"I have flagged \",len(cutTractList),\"tracts w pop\",popToCut,\" to reassign to tracts...\")\n",
    "print(tractReceivers)\n",
    "print(\"I have flagged \",len(cutPrecinctList),\"precincts to reassign to precincts...\")\n",
    "print(precinctReceivers)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "33897c0d-3254-4a76-9b82-314a8f320eaa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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eQLnoQQm9QtEopKR+Q2LiS3XeT0DABVUut9qsBGrLAPB0Oz+yWTYW7/61nzX7c7hneFSDiHxjoIT+PCYpP4nDBYeJ8ouirU/bKqMZFPVDSclBNM2dDpEPulwtCOFyvQCV3DCnPKKBgKDA4VXuf9E/H+IHmI2XoWkNkxjrfCW/xAzA/SOrrLV0TqCE/jzEYrfwYdyHfL7zc1cukE7+nZjSfQoToibgrj93p9E3V6S0odN5Ehl5X73vO6/4GPqSz8iwdOLSAS9yvDD3hHsIu/P40pW8JdAnGJ1OFSupKR2cA6Q3fraBWVP60TbQ8wxbND/UYOx5Rr4pn/v/up/4nHiu6nQVV3W+ir3H9/JT4k/sy91HgFsAk7tO5vpu1xPs0XARDOc6UtrZtn0KpaXlhSKcuVIq5ll03SEJLJZcdDpPhg3dUO+2fPv3q4SJL2rcPrV0KLdOmF3vdrRUrDY787ak8Mbve9Fpgtl3DKR3hH9Tm3UKpxuMVUJ/HmG2mbl72d3EZ8fz2rDXGBc5zrVOSsnmo5v5Zvc3rEpdhV7TM77DeG7ucTPdAs+NuGybzUb//v1p06YNixcvdi2fPn06TzzxBNnZ2QQHB2M2m7nnnnvYsmULmqbx/vvvM3LkyFody2otYtXqGHx8euHt3bVCSkRntsRKudMdz/18+xIRcRP1zfaDW0g4MAe9IRidZkCIihPeBeU/PxKBregX7Oi5ccLyerejpZOUU8zNn2+kyGTl81v70699YFObVAkVdaPALu08t/Y5tmZu5c3hb1YSeXBk6hsYPpCB4QM5XHCYObvnsPDgQhYdXORq0yOoB35GP1p5tSLMK4w23m2I9I2ko39HfIw1Dys7XTraX3/9FaPRSMeOHfnss88YM2YM4PghSk5OxmQyUVZWRnh4OKGhocTFxXHs2DGuueYa/vnnH9q3b3/KsZYtW0a7du1cyz799FMAdu7cSVZWFuPHj2fz5s1otSgG6oiGgVZhl9Ou3R013q4h6NOxP3061mw26OwlO/BkXwNb1DLpEOzFt3cO5uYvNnL9Jxt4YWIPpgxuf06MbSmhPw84kHuA/23/HytSVvBIv0cY32H8adu3923P1MFTebDPg3wS/wlf7/4agED3QPJN+axNW0tOaU6lXmuYZxgRPhGEeoYS5hlGiEcINmkjKT+J0e1G42XwQq/pcdO5UVRaxHOvPscFAy7AWmqlf//+XHzxxVx88cVMmzYNvV7PU089xbXXXkv37t0pKChw9dB37tzJFVdcwVVXXYWfnx8A7u7u/Otf/yIjI4Nu3bphtZ5I7PXIIw/z8CODufPOjSQlf0hevifr1/9MbJ+27N//GhI7RmMuP/18D716hVeYbSqR0nbiObYKoY527HaT4wDnwJe8EsIbo1ba1Facs7QL8mTRg0N5ZF4czy/cxbYjebx8RU983Q1NbdppUULfgikyFzFt0zR+PfgrngZPHu77MLf3vL3G2/u5+fHEgCd4YsATp6yz2C2kFaaRXJDMwbyDHMw7SFpRGjuzd/J3yd+Y7WZX2wUHFlR9gP0Op4J0t7B6+gKuGXElhb8mofMxEkEQX+1I4OFxd/HR4i8wJeejeeiZ+9Ucrpt8HXPmzmH5cof7wcvLi2+//Zbrr7+eHTt2uHa/aNEiwsL8MRrnYLMWk5LyJYWFRsLCCvj11wT6xO4iK9vGzoTD7NnzN2FhQU63h4aoNBlJc0TICJ1zneO5r09v/P361fh6Ngd0Oi/cxLmb+Ks54Odh4LNb+jNj+QHe/zuRfw7m8OqVvRokR319oYS+hZJvyufW328luSCZ23rexh3Rd+Dv7l9v+zdoBiL9Ion0i2Rk25GV1kkpyTflM3r+aMw2M7NGz0Kn02G1WzFZTZRYSyixlFBiLeHw4cPMOPI+I0b3w3q8DPPhAuwlVl754A1u6XMVprhsLOnFZM+KB+C7L+bw0NBbCRQ+BG2zU3DkCEt3rCLQ4EuEbyviLNtASkpKSnj11VeZ9fFEjh8HozGY4cM2ExwczPBhVp544gkee2wF7dt3ZPiwDsT0vofhw66ot+vTXNHrDOiwYbHZMehU8tqzRdMED43pzMiuITz1Uzx3fr2Fy2Na8/qkXngam5+sNj+LFHVGSsmTq5/kSOERPhrzERe0rnqSTUMhhMDf3Z9Qj1BSi1LpH94fN92psd1FRUWMuHUED1xzG2YfK60ecfSOb55yM57Bvkx9birLZv+KMGgE/180GzduxDvQlwT7Ya6+YAKmg3mUbM9i5ao/+TVhKfN/+YkSSxkWu4VQ/2DKrCZGDo3Dw9tOVpaN8PBwOnXqRLdu3fjyyy959913SU5OJioqikOHDvHiiy+68qi0VPQ6A5pdUmK24udxbk7+aU7EtPVn0YND+WjlQd7/O5HsQhNf3DYAD2PzCl9VQt8C2ZK5hX/S/+HJAU82usiX81PiTxRZigDQiVM/9BXT0UYbwiDbsXz27NksX+FwyfSaPASTzUyRpYQ7X/4XISEhXH/bjcycOZOtW7cSHhGBtNh5r3gA7xRa+Orr2cxf/DNFRUX8fMdH5B3JYex31/HiKwE8M7WEHTt20KpVKx599FFefvll3n33XdasWYOHhwd79+5ttGvTlOg0PdjBZLGAEvp6wajXeGhMZ9oFefDoDzu4d85WvrxtwFnnrGkI1L1bC+Tj+I8JdA/k2i7XNpkNL69/mTxTHkbNeIrQn5yOVmfXsAm7Kx1tXFwcaWlprL/vBz645mUuGj2KXbt28dFHHxEREUG3bt1YsGABXbt2JTq2F8++9gLGtj7szzvMpj3bSDi8lwlz7ib9MgNdwiPIOW5CZy9wVR/q2rUrn3/+Od27d+fjjz+mTZs2TXGJAEdIaJ8+fZg4caJr2YwZM+jatSs9e/bkySefBGDTpk3ExsYSGxtLTEwMCxZUM+5xBqTrK2+rq+mKk7iqTwT/ubwnqxKzmbPx8Jk3aERUj76FsTR5KRszNvJ4/8ebdIarRNLOpx2/XPHLKeFnJ6ejLcrO48GRNzNz7neV0tH2FO3o0zOGLZs34+vji0Dwwfsz8Xb34Lt3PuXPa1/FTW8gpziPtn6h5JUVYZN2/N29eKjzMK6/4ioMwsALHtfQb94yyhK+g5H/YvHixXz44YdMmTKF5ORkevbsSZ8+ffD19eWVV15h2LBhjXad3n//fVdkEcCKFStYuHAh8fHxuLm5kZWVBUB0dDRbtmxBr9eTkZFBTEwMl112Wa1L50mcP7qyZZQcbG5MGdyepbszeWHhLtLySpncvy1RwV5NHoKphL4F8Wfynzy1+il6h/RmctfJTW0OXgYvDLpTw86GDh2KlNI1wSnEP5AhXfqw3prIvn2OGO+8vDy2lxZx+cjxhASEEBoYzJHUFNLSUjF6uvPMqEvxDSsGoE0Q7Hjo32AzgX8k0qonv8DO8eIiPph4J63/aU1KuCCl6Ctmv1pQqRxdeHg4R44cISgoiK1bt3LllVeya9euSiXsGorU1FR+++03pk6dyjvvvAPARx99xNNPP42bm2NMIzQ0FKBSwY2ysrKzFg4py/PqNL+Jki0BIQSf3tKf535J4ONVh/h41SHaBnowoH0gob7u3DeyI34ejR+KqVw3LYSd2Tt5ds2z9A7pzacXf4qH3qPG257sPnjppZdo06aNy1WwZMkSAObOnetaFhsbi6ZpxMXFnbXN5b1ZKQEhmDdvHnFxccTFxTFp0iTGdxnOp6u+58VXX+JwdiqjRo9i/ievkJaWxk57DuN/+JCrFn3BweE9CPzPcwS++l8Cn/g/fB+/kauWvEdUp47c/fMMrDvn4r5V8MuuFBb98jNz5851CaWbmxtBQUEA9OvXj44dO5KYmHjW51QbHn74Yd58881KE7USExNZs2YNgwYNYsSIEWzevNm1rn4KYasefUPjbtAx/doY1jx5EfeMiCLleCk/b09j1qqD7EjJaxKbVI++BXC0+CgPrXiIEM8Q3rvoPTwNtUu6dLL7AOCRRx7h8ccfd702l5bSJljPjI/+i95o4MjWnTz+0lEMiZvZFbcae4e2joZCYrPaSHnmAAUBGcQH/EJJWRnf/7mSn3/4FU3TccHQ/lx19Xg+//gDPAID2H1wDze9/zBXZ67jwdvuRUo73835ln/HTiFQO8aBAwcYNWoUGRkZBPr7o2kaew+lsGHDBjZv3szkyZM5dOgQQgiklEyaNInDhw+zdetWhN6I3sfA9jX5fJ+Sxy9vP1Cpd5ydnU1gYCA6nY5Dhw6xf/9+oqKi6vaG1IDFixcTGhpKv379WLlypWu51WolNze3ynMrL4S9Z88ebr31VsaPH4+7+wn3XG5xLmvif8JiLar2uEdzNhDQMNXqFE6klGw5nMsXa5P4c9dRdJrgkh5hTBncngs7NU3+KCX05zhxWXE8tvIxii3FfHyxYxC2NlTlPqiKbz9/lWNr4gBol5PP33F7GCcl19zyL8L0ej6KcAj9nNzjfJRzDBsSmWPDet/zzA724sfNO5FIdJqG/kAyr/z7Bdr5+7I3PYuYtj34+orXGfXZLVxpH0haQSbBmh+pBRms2reeP7avQAiBxWLh4aLjuLu7E96qlSNtw8CBaJpGTk4OISEhLFiwwJVGoU+fPkRFRRGZdZTFGcdABxfc+B9aPzmbSy65hFdffZWLLrqI3bt3ExgYSLt27Zg1axaBgQ2fw2TdunUsWrSIJUuWUFZWRkFBAVOmTCEiIoKrr766ynMr5+RC2MlZSazc/imB/IqXoeS0x/UPgBKLB14eZ1cJqbmTkJbPtN/30CHYi3+N6kyYb+ONU5mtdpbszOCLdUnEp+bj52Hg7uEdueWC9rT2r/kddkOghP4c5pcDv/Cf9f8hzDOMr8d8TeeAzrXeR7n7oLCwsNLymTNn8vXXX9O/f3/efvttbJ6OW36fYdF4/hXHH4UFDBszhHbpWZQUlZD/yoNsTUhk8bzFdAjxY/KVw5m/aA0FV1zE8k+/Q9PrmPHxf8jJzuWZx98guk0YYe0iOHQkFYuXIG2QDf08N4xj27Dgi5+YcOVl/N81NzNmZ3ten/sHBndPjmWmc8O4wew+mMKiP5YxceJE3nnnHXJycoiJiSEoKIgDBw4wevRoQkNDOXr0KOvXrycdO0F6HReM8Ofx21sx9KYEAIqLi5k1axYJCQkkJCQwc+bMur8pNWTatGlMmzYNgJUrVzJ9+nTmzJnDrFmzWL58OSNHjiQxMRGz2UxwcPBpC2Fv23YNbY15ZJgG0KrNvbQP7XraY3u5++Dpdu6l2j0dxSYrH608yKxVB7HaJesOHOP7TSmMjW7FPcOjGjTbZG6xmW83HeHr9clkFpiICvbiv1dGM6lvm2Yzeap5WKGoFYXmQl7Z8ApLkpYwqNUgpo+YflazXqtzH9x33308//zzCCF4/vnneeyxxxh+SRcARl02hX/WHkSHIL1Y8vw7M3nnnXcYfM0DvPPDZPxC2vDuu++Sn5/P6q1ptI4ZSkb+J/SIiuLtt7/leFoKQkBC5jFSzHays7MxmUyMuXEiH374ITETB/HXPSvZunUrRfv/IS7PQKivkbS8EgSSfBOYS4vwcPdg1aqVXDd5EpdfejG9ortRVmbi9bdnsmHDesJCgsjKPk77dm25LNgT9qdzy/8NJyt4O6bsNPSBrRg6dCht2rThmmuucZ37yZkuAeLj47nnnnsoKChA0zQ2b95cyWVSn9xxxx3ccccdREdHYzQamT17NkKI0xbCNggTmZbhTBn/ZYPY1Jyx2SU/bU3lraX7yC40cVWfNrwwsQcFZRbmbDjMvM0p/BafwYguIUzqF8HEXuH1Ft++P7OQL9Yl8/O2VExWO8M6B/P61b0Z0SWkWcXQAw5/0un+AHdgE7AD2AW87Fz+FrAXiAcWAP5VbNsViKvwVwA8fKZj9uvXTyqqJqMoQ1624DIZOztWztg2Q5pt5rPe19NPPy3btGkj27dvL8PCwqSHh4e86aabKrVJSkqSPXv2lLO/f11OnzxBJh7aIW/qEys7GY3ynxXL5YoVK+SECROklFJ26NBB9u/fXw4cOFBG+PrIyOAAOX3yBOlhNMgLe/eSf837Vv579IXSqNfJGwfFymBvT+lpNMhurUJkdGQ76ebmJtu2bSvd3Nxkr+ie8vnnn5etWrWSQ/r2kN2ChdQJpK+vJoVABgRo0tNTyL/+jpKjx3jJ4GCdjOxgkN7emnzooWA5/e1wGRvrJiPbG+SdV/rKcA+97BoRKMeO85Y7f3xNvv322/KGG26QEyZMkF9++aV84IEH5JEjR+Qll1wi27VrJ7Ozs6WUUlosFtmrVy8ZFxcnpZQyJydHWq3Ws77mDcFPv/eVX/12V1Ob0eisO5Atx7+3WrZ/arG88oO1ckvy8VPaFJSa5czl+2X/V5bJ9k8tlmPeXikfmbdd/rI9Vdps9lof0263yxV7M+XNn2+U7Z9aLDtPXSKfmr9D7s0oqI9TqhPAFlmNptYk6sYEjJJSxgCxwDghxGBgGRAtpewNJALPVPEjsk9KGSuljAX6ASXOHwXFWbIubR1J+Um8OORFHuzzIAbt7EO1pk2bRmpqKsnJyXz//feMGjWKOXPmkJGR4WqzYMECoqOjEQbHzV9ZWQmL9+yhm5s7fWNiK+2vsLCQPXv2kJmZyfGyMpJzcvk94SA+7h4MvfgS+o+5hFHX3Yim05NsERg8vPD180MIwe0jBnHjjTcSFRXF0KFDGdErgo0bN9IxzJuA8A4InQ53PZQV2/nvJC8uiIkgLNCHzoaRhHm0wSDcsJa64e/tydxvCnAT3rgLfzIz7fy9vZTgTnqeuCqcoAA9j701k99++40777yzkv2PPPIIb775ZqXQxaVLl9K7d29iYmIACAoKanbVmax2N5DnT0bKhLR8bv1iEzd+upH8UgszbujDz/cNoV/7U0eZfdwNPHBRJzY+M5q3r40h2NuNtftzeOj7OC55bzXzt6Zit5851LTUbGPuxsNc/O5qbvtyM3syCnjs4i6sf3oUr0/qTddWzXvM44yuG+cvRfkwvsH5J6WUSys02wBcc/K2JzEaOCilbF5Txs4xjDrHtPUeQT0a7BhPPvkkcXFxCCGIjIzk448/ZsWmHwDYvGErRp2ezaUldImJwWQ2uwYS+/Xrx9NPP83IkSN5YMRgPv9nK9+tXM1dd93Fpm3b8AsMovuIMdilxCQ0JlxxBX/88QcPTr6CvIx0NsRt4LvvvmPixImM6jmM3zZs4LUn72H6Zz9y6ZSHSN69mvSdW3m2h0bojFyiY/rRbtjnvNMlk+DgYKZOncrMmTMpLjbz39fzKSgoYOLEq/h+7ud88+Ygnnx/L28/1oV7f9nLkq9fRlYIa0xKSqJTp04uQS8nMTERIQRjx44lOzub66+/3jVbtblgk25YrcfYmbztRC8O+4lyglI6igpKO9Jup6jQjk2GOlMxO9ZJO9grvpbld/uOyW9Wq5Un77mZwOBQnn3tHb7/6hP++m0hvv7+SLuNy668huHjriCqbWuW/fIdn3/+OTqdjv/973+MHTu2TueXllfKPwdySMsrZUdKHiv2ZePnYeCZ8d24dUgk7oYz//BqmmBSvwgm9YvAbpf8Gp/Ox6sO8fiPO/h6fTITeoXj427g8tjWeLudkMWj+WV8vT6ZbzcdIa/EQs/WvrwzOYaJvVtj1J870ek18tELR37WrUAn4AMp5caTmtwBzDvDbq4HvjvNMe4G7gYqFYlQnGBn9k5e3/S6I/e7d0S97nvkyJGuKkvffPPNKeuFUxT7Dohh6T134fbbH3RYu5oNCbtOGUgcPnw4c4ZMxLRhOxdu2I3postInfowbu06gACzlGTeeBdLCvPJLP6Zh+f/hsFq5cKe3Xnvvfew2WzMWRbHmDFj+GftWg4ePMjKlSsZN24cu/KMhL9dSF6ZxsaNG5kyZQpz5swh+c/XGDvkJ8ryPfj4yxKefljPDz9r3H/rdhZ835t2/XWEtzHw1fwS/P11SOs0itMeA8BkMrFlyxa+/fbbU87barWydu1aNm/ejKenJ6NHj6Zfv36MHj26Xq9/XbBKHyJ9dpF1qGYpL6QUbFx/LVZrzYuIr1+/Hi93IwXHMolf9RtZh/fTv28sQ4YMcbXZ/c9S/so+xsa1q9m1axfp6emMGTOGxMTEOt0FPf1TPGv25wDQPsiTf43qxF3Do846B7ymCa6IbcPlMa35cUsqX6xLYtrvjlxHO9PymHZ1b3ak5PHFuiR+i8/AJiWX9Ajjjgs7MLBDYJPPcj0baiT0UkobECuE8AcWCCGipZQJAEKIqYAVmFvd9kIII3A5Vbh3KhzjE+ATcJQSrOkJnC9szdzKA38/gL+bP59d8lmtY+XriuashSql3fVBlyfd8pYPJPbq1YsSs52A12bQy1qCaNcanyuuJvnvpdgsZlr1juWCDu3ISohHRnXE/tYn+JcUcnnSDubNm0f79u2xWq20bt2a+T+upX2bVnh7ezNmzBjGDQwhbfVvWDoO4Wixt8vVVHBsO4dKyvhqTj79uwXjXdQOWWbG53hXpn+yg10Hc0jNNhHR2oql2I3rHzqC2fxvcvNsLF26FIvFQlBQECEhIRw9epS2bduSlJSE1WqlqKiIMWPGoNfr6devH9u2bWP06NFMmzatXnuuZ8uF/aazL3Wz830RjsKBwvmIQAgNTQgQUJK1FE/9Mrr0H4DBo72jtcCZY9/5nArPhSAnO5OFv/3Bzfc9ws/ffs3QiddxKD0bD09Phl9+PRZTGaXFRWxJSOTgmjVcd911uLm50aFDBzp16sSmTZu44IKzT65n1GkEehlZ8fjIep1VKoRg8oC2TB7QlmNFJh6eF8d3m1L4blMKAN5uem65IJLbhkTSLugcj1Kqznlf3R/wIvC48/mtwHrA8wzbXAEsrekx1GBsZdalrZP9v+kvJ/48UWYUZTSJDd8vnCGnT54gtyWskdsff1Tu7tpNlmRmVtnWVGaSYcu3y1fmLTxlXVUDwOEXXSx7LlkrLRaLDA0Nlffff79s06aNbBvRRmqaJgHp5eUl/fz8pL+vp/Q2ImdPv9c1CDxlyhQZ2cpb6vXICy+8UKanp0sppZw/f77s0aOH7N27t+zTp49ctGiRy47je1fJ95+LlIMGecrlv3SSW78ZLw/98R/52guPS09PT3nxxRdLKaUcOXKkjIqKksXFxXLRokXS399fLl68WO7atUv27t1blpWVyUOHDsmoqKhmN0hbFevXT5d//R0ljx7dXeNtJk2aJLds2VJp4P3FF1+U7du3l7169ZK33367PH78uJz2xc9ywIAB8ssvv3Rte8cdd8gff/yxTjav2Jsp2z+1WE7/c2+d9nMmSs1WOW3JHjn23VXyszWHZEHp2Qc6NAXUZTBWCBHi7MkjhPAAxgB7hRDjgKeAy6WUp5+lATdwGreNonpWpqzkwb8fpK1vW74c9yWtvFo1jSGyiqLX1RSWF87QMnsV66oaAO795PMA/PnnnxQXF3P48GFSU1O5YMiF+Pv54u5mJDg4GHd3dy4f1ZseIRr3TP2c1NRUYmNj0ev1GI06nn06lLVr1xIeHg7ApEmT2LVrFzt27GDbtm1cdtllLjsCug4neuRneIkOeBaGkRe0j415n/Hjwpl4e3u52hmNRsaPH8+AAQO4//77CQ8PZ8KECSxcuJDrr7/+lJ5rS6NiCG5F7rvvPg4ePEhcXBzh4eE89thjaJpjZrLtpM9FXV0dI7uGEt3Gl7gGTh/gbtDx9Phu/PHwcP5vaAd8mnl5wNpQE9dNODBblNdRgx+klIuFEAcAN2CZ843cIKW8VwjRGvhMSnkpgBDCE7gYuKdBzqAF80fyHzyz+hm6BXZj1sWz8HPzazpjnG4aTadzfXHtsiopr/6LbbHZWLTvEMVWK1JKEg4kcSS/kOMGL/zNZbz00kt0797d1X7evHk8dfd1LFy5lfETLiMuLo6d+w4wqYueQo8QV56dV155he+/K2Lut6Us/DUWcETLlCcEq44RI0fyWJmB/37vz8KflzHugo7cdW8QWzcM5/PZ35GTk8N7773HsGHDKCgoQEqJj48PmqYxefLkSqmFIyIiSEtLq9GlbCy2bf+cwoIDztq3jpq3+Xk78a3Fx6i6Gbxz5sxxtbnrrruYOHEiPUZeia+vL0cOH3GtS01NpXXr1nU+F0+jHrO16s+b4szUJOomHuhTxfJO1bRPBy6t8LoECKqDjeclCw8s5IV/XiA2JJYPRn+At9G7Se2RdueXTBMnCmJXE5bm8uGftHpuQiJPHzedWNCxD/zH8dEy5BzF19f3lFQMk8cOwT28G5998jErV68hrPBXjn/9InNTTuzmueee4/KO8WQHb2X0xXE1PqeKOX5+X/o3ISHB+Pvnsz1up2tC1EcffcTHH3/MpEmT+OGHH3jnnXeIjIx0JUKr6rybAxZLKcePv3ZK7XJfP7BYPPD2rll90+pm8GZkZLjunMpDcHWaRteuXflp/nyefeZp0tPT2b9/PwMHDqzz+bjpNQrLrNjs0vkRbD7X+lxAzYxthszbO49XNr7C4PDBvH/R+40+8FoV0qZH7z6EzZ/uRuQOwDRyPB3f+hvtlBAzx6271qcjnxT4sf6bjS5nT8rRQtyPmQjxtRGotyGkdA0Gxs+dTqsx1/HazxvZk5TFTe/9hgSKCoIp3bUTzWrm4GdTSTTnk5PZgUOHdxPZOgAPNwPXXRxDh2A7Ni2cn3+4pdIvTKY5CIuhF5Gebk7rQNohucDElyu+YHK/y1iY8DtfvvAxKxOP8NPyYry9CzCZTNx3330sXbqU999/H4Brr72Wm2++mccffxxvb29SUk782tRXz7W+sFpLEAI0cS1DhjyPpukRwnE3Vh/zAKoKwf1hxXZCQ0OZeMWV9OjRA71ezwcffFAvx3M36FizP4eOzy5xLRMCDDqNmTf04ZKeTeTSPEdQQt/MmL1rNtO3TGdExAjeHvl2lbVWmwJhC0fv0QNTgQWhd/guDxW3Qqsi3a0EjAm5IGGPrhjKO19WhwBnF+goENK1uOjAZop1fiQbIynO2EmByc72TIvjuHhhiF9LbJtW7EouwdcIZhHCy5NH4uVu5HBOPu9/u5ZnrroQD82LsqIcp8GOh1JdLprNSg6jnJY5qizNXvcJlw56kKLSTCx2HZf1uZXg0EEkpm3j4RcnccMtd/HRRx8xbNgwVq1axciRI1m+3FHi8IYbbkAIwY033sijjz5arz3X+sJmc0yg0nReuLl5naF1zThTCK6m7QDggX8/wuuv/rdejlnOI2O6EBPhh5SOG0mblFhsdj5aeZDF8RnodYKhnUIoNdvIKiwjxMcNf09VKrEcJfTNiE/iP2HG9hlc0v4SXh/+ep1mvdY3PiGhHMNCwKUWbrrizGGE7z31GyND/fn4sQvP2PaZZzbwzfqdlH73EOayMmwFBfRNmc+cOXOwWq0Ez7qDscMHYdK5M/KlD+nc8cQ8C5vNxix/f35NNrFmzRri4uK49957KSsrQ6/XM7JVWzq20dP2Gh+efvppzGYzxcUleNvDaBfShf3pJdiFgVzhyZ/b5/LgpW9QWpTn2v+nn37KQw89hNVqxWq10rZtW6KjowGYPHlyvfdc6wurtQwAR2Rz41Ce38Vqr/8yhVEhXthlKCarDZPFjslmR68JPlp5kEU70lm0I71S+/ZBnqx64qJ6t+NcRQl9MyGlMIUZ22cwPnI8rw17Db3WvN4abx93wIImayZmAsdMy5pQnR8Y4K+//iKmdy8COvXCkLqbbbsO4O/r4coh/8ILL2Cz2Vw55u/9v7u5pO8QLugzgKTCHN55+zUevbIdwcHB/Prrr7Ru3Zo777yTL774kpe+vwmL1UypqZhv173FscIMps2/G4+lOsxmM3379mXTpk1s3boVcKRIqJgueOrUqUydOrWGV7B2ZJUcw13vhu9Zjs3YbA6ht9v1JOfmkVpQRHpRCRmlpWSVmckxWzhmsZNrlxQgKBQ6LJpGO2kl2t3ABSEBjGrXmlbeNb8bKK9alZqZR4fwkDO0rh3P/ZLA/K2pNW5fYlY1cSvSvNTkPGZjhmOy8YN9Hmx2Ig9QanEIh9GtZncZAoGtHqa9ff/990SEt8eQupuciH7cP244S35dyAsvvIDdbic1NZVnn32WDRs2YDaZyctIJSlhNX4FB9h+JA1fDwPhnaLp0+dEPMGnn37KggULSE1PZv369UyfPp3Fixezbe0ccs0v0rPzQgYPm8iWLVtcGSLtdjs//vgjq1evrvtJnYGs0kJ2bnC4gXIJokgXgc3YGqMhAJ3eH73eB6OmQwqB1W5HWguwWnOxWvKQ1lw0Wz7u9mMEAm/afNgWl3zSEQwIdHhiwRsrvthphRVhtZOEjvk2Az9mFkPmfgJMpeS6eRCt2ZnaNoih7SMwVHPnInSOz+2+pCMMi619yuzTkV9qISLAg9eu6oVRr1FqsZFVUIbZaqdtoCe5JWYsNolBJzBb7XQKbdrgheZG81OU85TUwlR0Qtd0cfJnwGJz+MxbhdYsgEqj5j36ilT0AwPcc++/WPfui+S36sGL057D3ahn0qRJTJo0iWuuuYY5c+ZQWFjIn4uWMOOWq7k8pjOfrY3j76QMzGUmfvtlMfc+8gCfr17I4sWLue6669i4cSNms5muXbui1+vp1q0bFouFF1/9hIR9Kei1y8jPz69k1+rVq4mIiGiU6lM26eiNluJFsUc/DOYU/Eo34lFagIGqSwCaMVCMLybNB4vmT6GxG0fN/ekkOzLAaCXM3Y1Wnh608fYkwteHMG9P9E7BLq/d26ZNG3YuXszjTz/DF19+gc7Hj1Qp0d/5bxIGD+Pqn36n+JP3MVrN+BoNzJg+nUmXnQgx9XY3kgl4ejWMyHq76RnepX7vFM4XlNA3AzKLMvlx80I6efYkPnEfSGfgiFMnpb3CNKUK4ildk5jKF1SY0FRBYw8dSeXoniLCe3rj6+GDrsQd7zY6hE640hicPJOu/FDSmdjqSFIWEFrjcQMBZBWbmLf0AEjH5Kny8yjfp5TSEaEpwe4w3plEC1fx8MN/fIG/0HP/c8/ibnQce83m3/j9t98pKs3hSNY2EuL3UVhyDAhk/YEjXDp2KBffeTtb12ziyinX0LFLF9JzMpm3fDEDxw5n1drVvDbrXZYu+g1PLy+uve0GHn9lKul5x3h0RnsM2mj+c8d89q//gdJWQSDtdHC3M+/dBzi8brZj/oC0I6UdW14ZpsN5ICTu0UE4zsS5Hokje4jdmUDMuc6RYswR/kN5AjK78w20Y5VWMILF83puHfys65ra7XZKrMUUmPIptdkQAoxC4GH0x8fgjVF3dkm2Ti4l6e3uxrNPPOEqJWm324nPPs7XaQEkvvwahwLDyEpPZ/JtdzDiuS8JtVmQCI7n5mGXHVi04givrctznA/ipEl2J54WO90r3m46cCZsKB9EL3/uXEqxyUqglxpcPVuU0DcxZcUWvp+2kRuPOWaHbvwnuwGO4oUHXuQdhjzsOLJF1xbH5CODuWZRQJ6axm6TiaeW7zuLYzmIyd/B8MLD+Ay7hlYh/gBkHU9n4/QP2Ri/j62H09i0chNWu50yi4VvC4vYnZFFn7GRTM98GXOQmaMZGbjf5MaxP4/x4o7HSXojiTZ3tuFjy3vs+2sfHZ7swO6kHeRl5ZFfms/Hme2wla6igEKO2t6kpPAMYxLeQE/n89PN5xGciD6qiBQgNUdcuHTOUZAamsUT78zKvVdN0/A2+uBtrL+UuDUpJalpGrFhwcROuc5hspSM+WMtK8pMHEyzcEhvwEuzgvRFYAedEYl0ibQmnFl3Kio5AA6h12nC1X8p/x2wuzo6dsqfnsUNosKJEvom5lBcNvpj3viPMCN8rOg0HUadEVEuDK4ejjhVKCouqtATck0mqdC+f3Q0R9NyOZqaS/7RUrwjNVfSKmcOLEdMu1aeCIsT64GsbRaO77TiJWomMvPuH8K+w3noNIGmOb/o2omEWVqF/TvivZ2JuJzLD65aR1rSeoz+XbnrgVtd+zWZShAIRt8xklfHXIcdyfYtO/j26/m89f5/uHHSXcQOvYVbBvXhzsm30S6yPQ9HP8bcdd9w/OPjPPfMC4y4ZBTbNm3jGd/HcWvl+OHy6+9H4bZC9j68F2mSPPXYnYyIuccRSaLpEEJDaDrQNDRNBzoNTdNA06HpNKQVx3qhIYTmaCe0E6+FznFthXN7tGon/dikJOXZtSTGNnwV75qWkgyoUFFcCMF9xZnERXZxhdo+dn0/7uhdvxlVzVY7Fpvj1/PBb7dxtMB0hi0U1aGEvgmRUrJzpSOS4NqrxmB0b9i3o1VQCPQ+u23XZx7k+M7Dp2SsrI4ObX3p0Nb3lOUV/cGLFy8GYMaMGcycORO9Xs+ECRN488032fS/1czbFE+JxyE+i/2L999/n5EjRzrEFAgNbcvgPhcDYM43EuC3iiF9xzLnm2956KGHOH78OCUlJSxZsoTCwkI+zM4jIzWdn778kZ++/JHU1FR8hvrwfz438fDVT7Nu3To+7PQhX639itzcXIYNG8addz5FVPuG98mfjE4I7IAuo4Rlf+wHu9OdJgFn7ngcT13Lo7oE0aXn6VM+nExtSkl+8cUXrvW7du3iqaee4sf5C/kmxc6Kf1J476/9zN2Sgq+7nm+u6YN3HWulFpZZGPL6cgrLToxJ9I5owhQg5zhK6JsIi8nGxkWHyEkpou/Y9g0u8nVFK09rUMf9nOwPXrFiBQsXLiQ+Ph43NzeysrIAWLM7DqHp2Jmwk5ycHMaPH8/mzZvROYVeVsizU3EAd+jQoWzdupVnnnmGb775hkmTJrlytFx99dWu2Pzw1uEEDQ+i1OqYWPTtt98ybtw4DAYDoaGhXHjhhWzZsqVRBl+r4riHRtejJjh6tEbtM/flQS2FvjZ5bMpJTU3lqquu4uuvv+bCPtF072xixK5MCgtMFOSWgcXO731zuLZb3YIKCsqsFJZZmdAr3CXw/SMb/g6nxVJdWsum/GvJaYrzc0rkuvn75aePrJIz7/lbrvp2r7SfRe3KxmbDwgNy5j1/y93r0s56HykpKXLUqFHy77//dqW7vfbaa+WyZctOaXvp0NHyhkEx8sBWR2raUaNGyY0bN8qjOaly+uQJ8uMvnq/xcSum1y0rK5X/evFu6dPFW8Z82VsuWPGdlFLK119/Xd52223SbrfLoqIi2b17d7ljx46zPler1SpjY2Ndx5VSyv/973+yS5cuskePHvKJJ56QUjpq0I4cOVJ6eXnJBx54wNW2uNQsM7KLZGZOkcw8Viyzj5fIY3klMje/VOYXlsmCIpMsLDHJ4hKz/PX9jXLLK+vO2lYpK1+j8jTPUkr5zjvvyOuuu05KKWVubq7s3bu3nD9/fpX7eH9zsmz/1GI5J6Hqz8jJ1+TFF1+UrVu3ljExMTImJkb+9ttvUkopN27cKHtE95KG0A6yXafu8ueff67TuZ0vcJo0xc27G9mCyDiQx47lKRzang1CEBUbQszotoR3PDduR0U99Oir8gcnJiayZs0apk6diru7O9OnT2fAgAEMHnYh87/5lJ+mPYYW2JWtW7eSkpJCh85tHXbYa5fJUErJ7+t+4b29M9j01zZ6D+7JF8O/oEuUoyTjAw88wO233050dDRSSm6//XZ69z5LPxc1v3Nxd3fnv//9LwkJCSQkJLi293Q34FnDNLnSqJ1+ILiWVJXHBhx++wMHDvDf//6X//7XkeJg6dKlfHagkNT8Mg5mOyqOmqt5b06+JuCYhFYe3VNOdHQ0S1asY8T01QyP0HPLHTcyzbcH/TsEMzhK5Uc8G5TQNzAZB/PZuOggafvycPPUE3txO3qNjMAn0L2pTasVmjN0z249O6mvzh9stVrJzc1lw4YNbN68mcmTJ3Po0CGe+c/zHDh4iJkr/sLXsIX2wWHodLoKrpua29E6IoSQa3x58sDztKc1P7/9HRcNGlepjbe3Nz/++ONZndvJVBXJ8tFHH/H000/j5uYY/C1Poezl5cXQoUM5cODAWR9Pp9PQajh2Uh1nymMDjiyhzz33XKVlB3JL+GTRZtdrCbT18Thl25pE95Tj6elJkM6Kv6eBvxNSKDHbeevPfXRpdZRlj46o3YkpACX0DUZxnom18/dzYEsWHj4Ghl7bmR5DW2Nwaz75UGqDzuDo0dssZze1vDp/cEREBFdffTVCCAYOHIimaeTk5BASEsLseQ7BmTP1LZ77+DXKUvPROWcNy2py4Vck93gOH/75HvNNi3HDyKPB9zPlkv/DYGjYeOza3LnUB5omEI0ce/jhtiO8+XOC605i2AURPHNhR3yMOtr6nir0tY3u2RW3Fcu8R8g/fJgHnnubRfk6PIzn5nenOXDulDE/hzi86xhzX9pAUlwOAyZ24OZXhhAzuu05K/IAOr3Ddqvl7ASlqspSc+bM4corr3RlhUxMTMRsNhMcHExJSQnFxcUAFHrr0YRGRGhbV5Hy0/XozSYTny/+gAm/TGSeaRGXuI3k1ysWcfuE+xpc5KuryFTxzuWtt95i8uTJtborOR1SJ9A3UE2OEouNvw8fY8WRY6xMOc7a1FzWpeXyx75MsEq6dw8mNjaMqcM60iPYu0qRr02VqnIGDRrErl272Lx5M4tmf4i0mnnrmpiGOcnzANWjbwASNx3FUmbj8n/H0rZHYFObUy9ouvKC4PWrKOUFxaOjozEajcyePRshBFlZWYwdOxZN0zBYJTcMiqHbkGiX68ZehR12u50/1i/if3tnkqbPpK8WzRNDnyK6U2y92nw6zubOpc5oAn0dXTdVYbPZGfrhGo5nFFdzXPjtpv6O+QSn4Wyie8rp3r07vr7eZGcfxlrPn73zCSX0DUCfi9txcFs2m5cktRihL89jZa8HQanoDzYajZW+8OVERkayb59jVu3MO+7FZiklOCKUMpNzVu9JveGExO1M++c14sVe2tGad7u+wehB4xu9ElF1mThnzZrF8uXLGTlyZKU7l/pA6gS6etT5IpOVl9fs55eNKVgKLSDgpnGdXakr7M74/Z6hPmcUeahdlSqApKQk2rZti16v5/Dhwxw9cgi3waEk5RTTs/W5EbzQ3FBCX8+Yy6wk7cjBbrW7esEtAZfLpJGzv66aswhTcSq+oc48A6LyncXx3Bz+98d0Fph+x8fuxROt/sUNF9/W4C6a2lLdnQs4ftQKCgowm8388ssvLF26lB49etR430KrH9fN4fwSpv69j3U7jiJNdgx+RiaP7cTzF3bEp44ToKqiuuietWvX8vrrr2MwGNA0jRkzZvLUVneSc6q5s1CcEVFffsL6pH///nLLli1NbUatKMot48DWLLYtPUJpgZmoPiEMm9wF74DmUSGqriRuzmTZ57voO7YdF1xVZbngemf3mm38PvMF3LzaMWXaa/iH+WOxmvnfTVeju7Aj3lGd+TzrG0q0Ui5zu4THxj1DQMD5F373+w8J9NqWS7b7id61hsSiCXRXdaRPr9NPXtqQnsuLfyWyb98xsEl8w724a1gU98dGoKtBj70xGPza3wzpFMQ7k2Ob2pRmixBiq5Syf1XrVI++Dki75OD2bHb8ncLRQ460tq07+3Ppvb1oFdWybjGF8/tuq48k8zWkKNfhpunQdzD+Yf4OO5wJfPYm72Cd33L6iV48OfQpenQ6fwfqOg1sw84iSyV3lrHURtfUMvamFEA1Qj9vTwbvrDzA0cMFIKBtlD9Pj+rMxI61m2HbGEQGe6oefR1QQn8W5GWVcHBbFombMjmeXox/mCeDLo+iY98QAlrVT33O5obOGUdf01w39UFwW0fvPCspCbvVzuE/N7J6ocOf3znNm0nXPsHoQeNr5CduyXSODKDzHZXTA6QeK4a3tqGvYoxi1vYjzPj7AMU5paAXxMSE8d/RXYgJPTU3UXOhQ7A3f+6qWToIxakooa8FGQfyWPPDfrKPOGKBwzr4Mub2HnQeEOaql9lSKR9vqI/B2JpyJOEQAMdTNzNjyjVYpRmjzh2PLu0Z+3/30zGy5xn2cP5ixTHtvaqP5esLdiGEYMzQtrx6UVfCvJq/e7FDsCfHi83kl1jw82w+tZTPFZTQ15Btfx5m/S8H8Qlw58JrOtGxb+g5N7u1LpR3mhuzRz9k8lh2r/mb0vxErNJMgPdAbnj3UTx8VZm4M2GVji+3rsok+BDVwZ/PJp59iofGpkOw4z1POlZMrKd/0xpzDqKEvgZkHylk/YKDtOsZxNi7ejb7TJMNwYmJSo13TKObnimvvcimX/4gYU0iJfpBfPHkJsL9jIy8K5rATv6NZ8w5hJSSnJQCIgBLFbHnQhNYbedWTHqHYEfx96ScImLb+jetMecg559iOZF2ianUirvXmW8DyyOTInsFNZjIn5yn/aWXXuLTTz91Tah57bXXuPTSSzGbzdxzzz1s2bIFTdNcedobmvJQwIwDGaz+zpGQ61haCprQ8A9v7SoNiDPWGinL06dXWi5x1Qp0FhCSSLsdfVoG3n4BCMp/TCRIRxk6L2mgY+t+ZOTrsNglJUf2kPDo73TqFQycKH1Y4YCu7GslFhtbNW/Suw52NJHSVa3IUeqPKpZXfu1sUPX6CvZWXu8sh2i3Mee9u/D2C+bKO95AAtvX/sSOdT8jNB3tuw/mgvH3kn88g+/fuRX/kLZIILRtD4Zc8Qh2ZxnGy/y9GenjdcKgCnZxkp2JmYW0L7BRpIMPLMXM2H7AaaujkV1ARkoBk2etdy2reF7l73d5VaiKz7WKhWKc7TRRueCNzS6xS4nNLis/l2Cz27HZK5STrPgeVDi+dP1zPNicd5KHj51NdTTFeSX0ZUUWUvYeJ2lHDsnxOVhMNgLCvejYJ4So2BCC23ojhMBispEUn0324UIKj5VRcKwMgJy0ogazraaZ/T799FMAdu7cSVZWlitPe0MPSErpuAa5GZLcjPJiEI7JLqn7zxRcX10dvYpEQk7NbOma+D3exenkJ9asfQzwvIjAoqt5vduTLT55mThlmTi1jYCcTQsw+7cmx1TC8mOFFB3eQWbcKjrdNhO9wYCtOI9t+SWYC024+YfT4/aZrn0kFZsRAlKsVqTZxjCL7iQDTqokVi60Hjpeb6PnYCdvjB6GSnlwhIA2nQLwzrOg00SlCWWuKmMC58SoE2Jsd5SzxYbdNWHK7qqFUv7ccRydcFQVK3/Uaxpu+vLnAs3543Dix6LiqZw4pxOn5XjWt50/o7uF1eg9VFTmvBD6g9uz2PFXChmH8kGC0UNP5wFh+AS5k7r3OFt/T2bLkmR8At3xDnQjO6UIq8mGzqDhG+SOT5AH0cPbED2iTYPYV5vMfrt372b06NGAIwOiv78/W7ZsYeDAgQ1iWzk6veNL3CqqgJjRPQBJQU4WmhD4hoQ6SxI6hKO8HKGm4VymOUsjCmfJQMBZtlAIjcxlf2Cf+RHu999L6/HjXftytXeWIiyvP3h4VDa6oGCiFi85UUKxvB6pVkH8hMafr7xPhwWzWfvIUPxC/J29U2fJQir3Vu12e6W7quuuu841OzcvLw9/f3/i4uLYtGkTd999N+AQvJdeeomrrrrqlGuWmprKrZveZuqMF3jnnXdYPO1SJk/+irs/e4sxY8ZUapucnMzE1d6sfnXcKfuZOGMNBh93wm7rW6P3KgwYfroGfWq0G0ULosUL/YGtWfz5aQL+YZ4MmNCBdj0CCW3v40q72398JKWFZpLiczi88xhlxRa6DAij66Awwjv6O0WrYalNZr+YmBgWLlzI9ddfT0pKiitPe0MLfXmPyzfQk079ujiXdq2XfZeF+lFiLqDkkxkkfjULAHtgAP0XLUbTn/oRFY7uIPqAM9evDV/7BwD//DMLwgNxyjzlRXlFhceF3y3FN8SNrLxUfts4m9ufuIILul+Kn5c/jz32GH5+jrkR0dHRbNmyBb1eT0ZGBjExMVx22WXoT7K1tlksk5KS6NOnD76+vtzyr6dI1rdDpwmO5pcRdA5ExiiaLy1a6C0mG6u+20dYB1+ufLQPekPV2SM9fIz0uLA1PS5s3cgW1r5u5x133MGePXvo378/7du3Z8iQIacITENQH4VHqiNs6FDiO3VAlJWB3Y53eibkFWApLsbNr4qJZ47ud432besSCNmZBNi/xHaa2tLZ2Va2rcnixpsCmP9jPu7F/wHgj02HmTzyaX744QdXlk1PT0/XdmVlZVXm06lt/v3w8HCOHDlCUFAQW7duZeQlEwi4ZQb+fr7YJfRo3Xxj3BXNnxYt9NlHCigrsjDypq7VinxTU9vMfnq9nnfffde1bsiQIXTu3LnxDK6l0ksp2Tx5EvqkI44BTU0Q9vLLRFw6wdXGPTiEgYuXAHBg7tdY/juNsssnVC3ygMuJXAOCBneibN0e2nb9AEPrrhUGWe3OUFHHYO5bb/+b5199m6KiIty9v6RNlw9I2XclVnsxa9asISwsrNJ13rhxI3fccQeHDx/mm2++OeXH9myyWJYXJenXrx/+rSIIsOSw46Vra36xFYpqaNFC7xfq6HkV5JQ1sSWnYikqRNrtvPzM07z8zNMArFq7lnf/N4MvPvqQw/sTCW/lGOz88bvv6NG1K+bCIkpKSpA2K14ebixbtQZNCDq2bYep0DlQXE1P12aX2NFViGSoEAnj0szyCAx7xZdIoPB4ZbfSmSjLPU7+7t0g7Xjs2oveLimIao/vocNkzJ2D1GtoOgMBvXqD3Y7O3Q03/wAs+Y5UEgG9Y8jbtxeP0DDcAk4qCl0LodcMDvEM8QzAN6RDlW0WL15MVPuOXDX+GlauXIm3uw/dIqJJ2qPHLs1899133HDDDZW2Kc+XvmfPHm699VbGjx+Pu/uJeRW1zWKZnZ1NYGAgOp2OQ4cOkXf0CCF+auBRUT+0aKH38nND0wRlxZamNqUSaV+8SJsj752y3JBsRTtkxvh2W55bUErcURsCiPTX+HiiO8a325CeZ2fsnBI0AW18BHMv98Dt7TMPElulgR+PvcVxa/s62V4+tnEmEi6biFdOLuD4kJkuGMigL2cTFxOD99Y4irbGAVAeY2QXEPLVV+gMRmxA6SuvUQqYDXp67dhROapIiBpVmAIQBkekjd1UWm2b6nrf19+iw2Y18fPPP7N169Yqt+3evTteXl4kJCTQv3+V+aQqUV0Wy9WrV/PCCy+g1+vR6XSMv/d5DrqfeQxCoagJLVroAfRGjeK80zhnm4L8FCx2N7I6PFBhoaRze/h4BKQBrz/sWOZaKyBdSozt4Z+uR/DP+YvjYVdgMQSRXnHfoopQxvxDtM5dSOvgvXgFBFcKYXM9uPzMJ0LeKu8TdHqNQVdeUKNT1ErLsAPafXeBptFlksMFEf7RhxTs3AESiuLj0Tw9saSl4rV9J8VJB4m8/kb2Z2djN5koXb8enyNpSIsF3E4MRgohHEnRa4Bw9uhtlurv6qrrfS/8M4aEbYfo1q0bERERrvYn50vft28fkZGR1e6/Jvn3J02axKRJk1yvX1q0i73bUmt0jgrFmTij0Ash3IHVgJuz/Xwp5YtCiLeAywAzcBC4XUqZV8X2/sBnQDQO5bpDSrm+vk7gTHQeEMaedRkMvKwDvkGnljlrEoQOmzDQ5vYX67SbmtYmytu6GH5dSKe+YbS5/LI6HbOckyd4lTN9+nSeeOIJ/orpjW9Ue2Lv/xd33nkn2z75HKvVyi233MIzzzxTaV/JP8+ndPtOpNWG0ceHns86ClBv//eDcCQNm9WKroLQo2k1dt0Io2M7aa6Z+85ms2GxWsgrysUuNTasSmTKDY9UanNyvvQPP/yw3oqIlKPThGuSkEJRV2pyH24CRkkpY4BYYJwQYjCwDIiWUvYGEoFnqtn+feAPKWU3HHNX9tTZ6lrQrkcQdrvkWFrzSXEqNT0ajVfBo9xPbbPWnwurfIJXRVJSUli2bBnt2rUDHH70H3/8EZPJxM6dO9m6dSsff/wxycnJle1zDmTaT7JPON1E6X8swW61VlhR86gbrVzoLTW7q0s1fcOTTx5g66b++BoLuOuRkdx7772V2tx8883s2rWLuLg4tm3bxpVXXlmjfdcGvU5gUUKvqCfOKPTSQfmUUIPzT0opl0opy799G4CIk7cVQvjimLvxuXNf5qp6/Q1JYGsv9G46Vs7Zyz8/HSAtMRdbU+f5ELpGFnpHtSVpNdfL/soneN15552Vlj/yyCO8+eabCCFcU9iFEBQXF2O1WiktLcVoNOLrWzlUUNM7/OjSYq20XDh78aVTX2D/Rx9U2ECrcWHtcqG3m2rWo9fsx8kzBXJMu5dj2r0MiXm4RtvVN3rVo1fUIzXy0QshdMBWoBPwgZRy40lN7gDmVbFpFJANfCmEiHHu4yEp5SndayHE3cDdgLNHWD/4h3lyxUOxbFx0iB3LU9i+7AiefkbG3d2L8I5NVBxENG7+dF252NWT0Fc1EWjRokW0adOGmBhHARDhUHmuueYaFi5cSHh4OCUlJbz77rsEBlauoyv0jtBXaTtJ6CuGLFYYjBVC1LhIuaZ3nnsNe/R24YHZ7sUNI5+oUfuaciK3y+ny7Zx4XWaxY7M7Qj8bu+6touVRI6GXUtqAWKe/fYEQIlpKmQAghJgKWIG51ey/L/AvKeVGIcT7wNPA81Uc4xPgE3CUEjyLc6mWVlF+XPFwH8xlVlJ2H2fFnL1s+/MwE+5vmjStju9t3U/xZD95dVP2f1j8N+/OKsJs/ATjm78SHx/Ptm3biI2NrfUxT54IZC0tZc0Fg3g2PoEPOnVkW2ws5vR0PPQGpEHPpk2b0Ol0pKenk5uby7BhwxgzZgxRUVGufZbfcRTP+oQtX37lWq4vdfTCPV9/jbaXX3HCiNr46N0cIY/Hn3+PY/95/8SKituXZycD+jmjedZPHQhSVkgtcyJRWvlzcSKzGKLiuvJtpKzi9an7y/IM4N7Rj2PVKn8d3Q2aEnlFvVCrqBspZZ4QYiUwDkgQQtwKTARGy6rvpVOB1Ap3APNxCH2TYHTX07FvKMkJx9j7Twa716U3yWxY6unLe3IitHnzTtxUVZyyf8PVE7g1900OdbiN4r5TuOKKK85K5OHUUMT83FzsRiPpFgs37D8AQJbVytUZ6Sx5+UVmf/st48aNw2AwEBoayoUXXsiWLVsqCX1Q/wGkdu+CLK58o2f288USHEzXy6+oFF4paiH0/jEXk3PZ14iikgrRRaJCkFHFjFqCguLD6ISFo6aBPLZkHkGe3rww5greXLmEtAJHuGix2YyX0Y3/XXUzFrudD9Yt40B2JkII7h4yit5tnGMUzpw/rtw+FY5Tvtg7/TBtErbwzOBQzIGhFXLwQOcwFV6pqCcct5TV/+EI7vB3PvcA1uAQ93HAbiDkDNuvAbo6n78EvHWmY/br1082JBaTVc5+dp1c8M7WBj1OdaR9+G9pfSGwTvtISUmRo0aNkn///becMGFCpXV2u11GRETIxMREKaWUJcnbpXzRVyZ9/aR85pln5LPPPlunY5ezYsUKOapfP7m7azd5YPaXruXt27eX2dnZUkopX3/9dXnbbbdJu90ui4qKZPfu3eWOHTvqdNzE4SPknuheddrHmXj77bflDTfccMq1lVLKRx99VL788stSSilnzpwpb7vtNimllJmZmbJv377SZrPV+Dj5S5fK3V27ydLdu+vHcMV5C7BFVqOpNXEWhwMrhBDxwGZgmZRyMTAT8AGWCSHihBCzAIQQrYUQSyps/y9grnP7WOC1s/9Zqh8kUJJvJqh1U1UqqnuPvtxPXlV64pOn7Nuc/mxN05g3b94pszzrhWp62A888ABFRUVER0czYMAAbr/9dnr3rqPLrBaDsWdDdYPN4OgY/fDDD65rWF020Zqi9/cHwJabW3fDFYpqOKPrRkoZTxWJTaWUnappnw5cWuF1HHDmKYONhN0u2bw4CZvVTtvugWfeoBlSXcKsck6esl8+iXTHoXQ8PT2Jjo6uFztGjhxJh+efo+SpZyoJfcXwSW9vb3788cd6OV45ooFz71eXTRRO/RGtazZRXbnQ5+XVl/kKxSm0+JmxFZFS8vfs3SRuzKT7heG0iw5qGjvq2KM/XSI0q9V66pR95+EW/rO73nvz5W7nxiwxWJvB2NpS2x/RumYTLRd6qxJ6RQNyXgn9vg1HSdyYyYAJkQy8LOrMGzQYFcI8zoLqpuwD/PXXX6dM2deEwC4lv23cy7oPrq+T5c2CBhT62v6I1iabqN1spmjVKmSZCew2pM2ONDtCXlWPXtGQnFdCfzTJEZ3iHeDexPHJDXfc77///pReu9A0Vh+20SrQr1K0S71QPieghknG6uWQOl2DCX1tf0RLSkqQUuLl5cWyZcvQ6/X06NGjyn0f/+JLst97r8p1modnlcsVivrgvBL62NFtOZZayIo5ewHoMbQJQiudiHqIo4fKCbMAvvrqq1OPpWmMjNSzYNRN9XLMyjt3Pja266YJqOpHNCsri7Fjx6JpGm3atOGbb76pdvvy3nvUkiWO9A46nWO8QadHH1rTzEUKRe05r4TeP8yT4dd35YfXNlNS0HQZLevqo68twlkUW8j6T7sgnD16SSP26BvQdVORmvyIRkZGuiapnQlhdLwPxrYRrvTJCkVjcF4Ivc1mJ2N/Hod25LBvw1G8/Iz0HNYwhb5rhqi3Hn1NaOwflgZH1zyrhZ2J8pQO0mJRQq9oVFqs0FstNo4kHOfQjmyS43MwlVjRGTTa9Qjkwms64eFjrNReOnORaI1QDFyiQwjpyMDYGG4I51iEaAg/evn1asQ8ceJcFXpDefK25lUIR9HyaZFCf2h7Niu/3UtpoQU3Tz2RvYOJigmhbY9ADG6nikR2SiF/fJJA0fEyBl0RRd9L6laF6UxI4bTBbgXNePrG9XE817y4+r+LODGg3YhO+hpWuWp2lAu91XqGhgpF/dLihN5cZmXZl7vwD/NkzO09iOgacNryd0W5JpZ/vQdTiQVNJ0iOz2kEoXdedrsFaHihL4+MaRh3UeMH0gud0wVyjmV2LJ/oJW2Nl6JaoYAWKPRp+3Kxmu0MvaYzbboGVNmmtNBM8s5jHNl9jOT4HOx2ydBrOrPx10P4Bjd8FSqXUFktiEbQeVd5wIbwr7hmTNX/rqs9pMF5/cxmV876c4HyqBvN2ChvukLhosUJvf00xRqsZhs7V6WxeXESFpMNTz8jXQa2ot+49hTlmjAVW2nXo+HTIgi944tuNZkxNGr4dAP0fl27bMSoG2fqYXtxMdq5JPRO37waiFU0Ni1O6CO6BuDpZ+T3T3bSdWArWnfxx2KykXEwnwNbsjCXWmnfK4hBl0UR3NbbdetvcNfh5qln99p0Og8Ia1CXQPkX3Woy0RhfeekahK3/c2oK10n5YKzd1MyKvp+B8h69UD16RSPT4oTezdPAFQ/1YdOvh0hYk0b8ilQA9EaNjn1D6X5BOK27+J8iUB7eRvqObc/6BQfJyywhoJVXg9lYHtduN9dPxaczUu4/r2Nlq91vTMOUnuZw09jtSCkpO3KYAEB4NWImUF15RarT+7pPLszy0ksv8emnnxIS4pic9Nprr3HppZdisVgcBcy3bau2gHl9YDebHa6uWuTCUSjqgxb5iQts7cW4e3phLrWSn1OKwajDJ8gdnb56oZNSUnDMUdHI6NGwl0U4KyrVtI5pnSnv0deh812Unob48mvcT1ruAVh1GuH9B5z9zmtJedFwzhCmeHJhFnDUtX388ccrtatYwLykpIQePXpwww03EBkZWa92S7MZYTSeUwPIipZBixT6coweekLanrlKT9nxY2z+/Qi71uTS+6IIvPwa1u9boDPQGkhffBcWvRsSEBIKnTVTfXQawtkLFxUSoInyitvlyyuUqju51J3AUZdUINHZrHQAvjLpmb9wuUPwJchTxlFF5deu9QKkxP2ld5w9Ut2J5YAUAnanI3enO7dzLG9XUsTP109AV89x7+Wum0OXXY7QRJUBP0ctFn5ITeGe4BBmHzvGnuheZGdlUqzp2PPV7BMNpSQ9P5/M/Dx29uhJkc0GGRlkjBtHqe6kr0etIotkpQfX9kJgTk3FWCFfjkLR0LRooa+W0jxI3QKpmyBpNW5HNnKB1BHhH4shty9ZK4cROvKyBjv8dr+epAcMJAgLOgQaIDWw2Z0Cqukd4gmAcD0vl3THg6BSBdKK7R2rnesdr1OMrTnoHU0rm7liqypDLkWVjxK3wlzcDUancIvyCnmOHyBRuf1+gzsbW7ejrMyEl1f9jjj7T55MSdwOKHfdnNxDFvBGwi6e6tadYqsVUViIPjQUrbiI744e5deiQqJ9fXmmU2f8DAYmhIWyctduRhzYT5nNxrNduhJSUYgr7t9VDrDCwVyLq2hXAbvJhDUtDXNSkhJ6RaNy/gi9qQg2zoKdP0K2I6kZQoNWvTD1/ReZ+48SXLAKn6ObKcv4FhpQ6Avcwnmk91v8GtWOAe0br/jJ8EY7Erw9fzFvAVZr/ceMew0eTOflf1e7fvHixUQtWcJVH37IypUr8Zo+nc6LF/NcZibvBQcjhOD555/ng4wMvvjiC9atW4ffhx+S+dVXrgLmN348q94zfZbuTCD52mvVhClFo3P+CP2Ce2DvYogcBr2ugYiB0KYvuPngDrQHij69CdIWcyzkChoyE47N6QLQ1XFwtDmjd6ZGsFobf7r/6XLKl3PXXXcxceJEAL6tQQHz+sAV/6+EXtHItFylOZniHPAIhFsWwvAnIGoEuFX233unLXY8XnR7g5pSLvR6XcsdlCv/EbM0QI/+TEybNo3U1FSSk5P5/vvvGTVqFHPmzCEjI8PVZsGCBa6Siu3atWP58uVIKSkuLmbDhg1069at3u1y5ehRM2MVjcz5I/QX3A+lx2HutZC05pTVxzYuR0pBeuB1+PVs2BK3NuekrsZIoNZUGJznZrE0n97rk08+Sa9evejduzcrVqxwVYZqkALmVeHKXtl8roni/OD8cd10uwxGvwD/zICvL4eHdoB/OwBsZhO63x91xKr4R5L+/XtI6Yx2sTsfpXTEsUjpjIiRzrDF8ueOw0hwxqtrztgXAUJzJhZzPI8oLmNMsB96rWPjX4dGQu/M6/Lxmk34GXSuId+Kj4UWGyZnBBDSziXtwrlk6OB6taNiTvnqioI0RAHzqnClKbYpoVc0LueP0GsaDHsMvMNg4QNQlO0S+tKMVHxkCpqw0+bQGw1uSnvgmqOQMugioP5dBM2ByKAAtFI7n7WqPkGczmbF3TlprNTNnV1H0rmksQxsAlyuG+WjVzQy54/Qm0vgj6dh+xzwawthPV2rvNt3pPTBXdiKC0EIhNAcmQaFcLzWhGu549HxGs0RZogQrrRhCEevH2kHaXfGt9sBu3OZJGH9V/TZ9g5GXWPW32tcRg8ZwIHSUuxV+OjLIw/dPT3ROXv+V8xdSJGuheeAKe/RN8G4heL85vwQ+tJcmP9/cHA5DLrH0bM3VJ7j6RHSCkJanXY3J0+pB5gxYwYzZ85Er9czYcIE3nzzTQDi4+O55557KCgoQNM0Nm/ejLu745glvo7jaC1c2Dw9ap4J1CAlliaqBdtYKNeNoqlo2UJvs8Kunx09+dJcuHwG9L35rHd38pT6FStWsHDhQuLj43FzcyMrKwsAq9XKlClT+Oabb4iJieHYsWMYKmYsdEZdCK1lX/7acD4JvXLdKBqblvvNOp4Esy6En+8C3zZwz+o6iXxqaiq//fYbd955p2vZRx99xNNPP42bM1VuaGgoAEuXLqV3797ExMQAEBQUVCkNgJCOL/q5WhKvITBIO9aWLvTlydiU60bRyLTMb9aO7+F/sZB3BK79Cu5eBa161WmXDz/8MG+++SZaBTFKTExkzZo1DBo0iBEjRrB582bXciEEY8eOpW/fvi53jt1m5WhWMrIkFwBNa9mum9pglBKL1sJ/+PRqwpSiaWiZvoM9vzoePQIcM2Dr2FNcvHgxoaGh9OvXj5UrV7qWW61WcnNz2bBhA5s3b2by5MkcOnQIq9XK2rVr2bx5M56enowePZp+/frhnTGPQQfmUT4SoDecO0UzGhoDEksLv8MpS0gAwF5S0sSWKM43WqbQT/oc4ubC0ufg14dgyvw67a66KfURERFcffXVCCEYOHAgmqaRk5NDREQEI0aMIDg4GIBLL72UNRs3cJe2injvrhTHTMHNP5y+XlWXOjwfMUCL79Fb0tIA8Bo0sIktUZxvtEzXjcEdBvwfBHeBA8sg/gc4vB6y94G5uNa7q25K/ZVXXsny5csBh7vGbDYTHBzM2LFjiY+Pp6SkBKvVyqpVqwgw5NCm7Cgeo57igov/Td8B19b3WZ/TnA89emlz1AUwtG3bxJYozjdaZo++nEH3wG+POwZky9G7w4A74ZJXqkwlWxvuuOMO7rjjDqKjozEajcyePRshBAEBATz66KMMGDAAIQSXXnopgyOKYR9YvcLqeFItEwNgbeFCj90ZbdXSz1PR7GjZQh97I/SaDDn7oCgTSo7D5s9g/UzHj4BzZmxtqDil3mg0VsqIWJEpU6YwZcoUADKPp+I/sw9lwoiHT8hZn05LxghY9AaklC22ApMr2kYJvaKRaZmum4ro9I5ZsK37gqUU8tPAJ9yRCqGROLJvFW52M0eu+orI1l0b7bjnEuXlsq0td7Kw6tErmoyW1aM/sgFWvwVZewEJ3qEOUTcVQsomsJkgoIMj5FLfeBEv1sJMAOy+DZnl/tymPNulyWrBYDSeofW5ierRK5qKliX0P94OhekQ0t1RVKTwKOQmO0R9wJ2OgiOt+9TZN19bNKMj7/3xw1shMrZRj32uEBTkqLR1eP8hevZsmYneVI9e0VSc0XUjhHAXQmwSQuwQQuwSQrzsXP6WEGKvECJeCLFACOFfzfbJQoidQog4IcSWera/Mhe/DN6t4Pgh6Hsr3Pwz3L8e7l4J415ziH8T+H+D4r+mUOdFl15jG/3YzQWbzUafPn1cVZ3AkSeoa9eu9OzZk9XffQ3AzmN5ABw5cgRvb2+mT5/eFOY2COVRN7TwMFJF86MmPnoTMEpKGQPEAuOEEIOBZUC0lLI3kAg8c5p9XCSljJVSNmxFj96T4b51ji/S9q8b9FCno6Ko7dm7hna5u3nuYBcuvGA0PXv25MknnwRg7ty5xMbGuv40TSMuLq7J7G5IyvMElVMxT9CuXbu45/bbADA78wA98sgjjB8/vilMbTDKk5kJXcsfGlM0L87oupGOChxFzpcG55+UUi6t0GwDcE39m3cWeAWDV4gjHXHsTdB+SKMcNuPoAbJS4/H2DuLjT2YTGmQk/8gWun8/kWXJdrYfsp6S/Oymm27ipptuAmDnzp1cccUVxMbGNoq9jUl5nqCpU6fyzjvvAKfmCQoJawVZJuxWG7/88gtRUVF4eXk1pdn1j7NHr1w3isamRl0LIYROCBEHZAHLpJQbT2pyB/B7NZtLYKkQYqsQ4u7THONuIcQWIcSW7OzsmphVNYWZjlDKwI4Q0ni+3pJvrydm8e24fTKRrb98zVNtEvArO8Yhn07MyB3OCy+8dErys4p899133HDDDY1mb2NSkzxBO/fsAaCsuIg33niDF198sanMrRVSSuxSYrVLzHY7ZTY7pTY7xTYbRVYbuXkFbNm5mw9/+Z3rfFrz4OMvIVt48jZF86NGg7FSShsQ6/TDLxBCREspEwCEEFMBKzC3ms0vlFKmCyFCgWVCiL1SytVVHOMT4BOA/v37n32QXd5hsJY5Bl0L0h0FwOuQ911KyfL1b5J0PBE7dr7J3Ul/j3B6ugUhneUFpZSgZfKnvx+v/K6jy30DeNkq2L93Oy/0u559X33FmjVrmDp1Ku7u7kyfPp0BAwZUOs68efNYuHDhWdvZXKlpnqBJkyYhZy/ihS+/ImTgSC5bvJqkhP3o3N357fslnDyysjusNZrNhqGRcrtL4SgLKYXA7iw8Y6+NYPuFg5/jqerRKxqbWkXdSCnzhBArgXFAghDiVmAiMNrp4qlqm3TnY5YQYgEwEDhF6OuNVr0gqDMkzHf86YwQ2t2xvFWM8zHa8QNQQx7eX3lS1F/FyfxVnFy5UYA/BXEFmLwLOBa6h6I9RRSZjrHj8NdkFR+vMvlZ+cSgjRs34unpSXR0dF3PvtlR0zxBRqOREXt38MfeBJL/WUnyt59jKSkGoeGt0xF1yYRK+x2cfphiTUc/aiH0Z9V9kEiEo/qvLK8EDEK6qgA7n5evd7wWGmgIdO7utPb2om+XKH7EyMcp2S12Qpii+XJGoRdChAAWp8h7AGOAN4QQ44CngBFSyirT8QkhvABNSlnofH4J8J/6M78KDB7wwEY4dgCO7oSMHY7Hfb87/PblBEY5Rb+3868X+LQ6JSpHCMFjAX15O3cbX8Q8QqeoS9BrBow6I0LoKpQX1PHs7hf5dt9cCl8spKysDGuBlZSPU/AKblVl8rOQEMcs2e+//77Fum2mTZvGtGnTAFi5ciXTp09nzpw5zJo1i+XLlzNy5EhXnqBv7rsVcf9trm1feuklvL29efzxx5vI+vrFeiANvRJ5RRNQkx59ODBbCKHD0WH5QUq5WAhxAHDD4Y4B2CClvFcI0Rr4TEp5KRCGw9VTfqxvpZR/NMSJVELTQUhXx18v5xixlFCY4RT/eDga73jcXcFd4hUC4bHQOtbh+gmPBd/W9IsaB1u3UWIuJMA3otrDvvnGG7z5hqO4+MqVK3njrbdInXwErx1dThG18syWdrudH3/8kdWrG+4mpzlSXZ6gloxNSgzKPa9oAmoSdRMP9Klieadq2qcDlzqfHwJi6mhj/SAE+LZ2/HWpEM9elg+Zu06If3ocHPzbWdAb8AqlkFIIDeDXrC10LT5KmGdYjURJOH0FUeP7c2j2ripFbfXq1URERBAVFVXfZ9zsqGmeoHJeeumlhjeqEbFIVI9e0SSIalzrTUr//v3lli0NO7fqtJhLIDMB0rdDehxJib9ydZg/VueXNMAtgO5B3eke2J3uQd3pEdiDCJ+IU8TfarfS55s++Pv0Ys3V3zbFmSiaEY/vTWHpsXziL2x5YzGKpkcIsbW6uUotKwVCfWH0hLYDHX9ABz7iH0sJ+3IT2Xt8L7uP7Wbv8b3M3j0bq90xGOhj8KFbULdK4t/Ox5EdM8i75ffWFWfGKiUG1aNXNAFK6GuIh8GT2NBYYkNjXcvMNjMH8g6w59ge9hzfw55je5i3bx4mmwkA4QwKPGrz4t3ko/T28aS3jwchRlUr9nzEKiU6JfSKJkAJfR0w6oz0COpBj6AermVWu5Wk/CT2HN9DQnYCy44mUuLRhzeSjrrahLsZ6O3jQW9vT3r5eBDj40mYmxL/lo5F9egVTYQS+npGr+npHNCZzgGdubzj5TzrXF5gtbGzsISdhaXEF5USX1jC0pwCV2h3qFHv6vHH+HjSy9uDcDdDi49EOZ+wSoleU++novFRQt9I+Op1XBjgw4UBJyZqFVltJDhFP76wlPjCUpYfK8AZ70OwQe/o+Tt/AHr7eNJGif85i/LRK5oKJfRNiLdex2B/bwb7e7uWFdts7C4qY0dhCfHOO4BVuZnYnF3/QIOO3t4O4e/l/AFo525U4n8OYLFLdOptUjQBSuibGV46HQP8vBjgdyJzY4nNzp6iEy6f+MISPkwpdJXd89fr6FWh5x/j40l7Jf7NDtWjVzQVSujPATx1Gv38vOhXQfzLbHb2FJe5ev3xhSV8kpKNxTkvwlev0cvbs5Lrp4OHG5oSmibDqiZMKZoIJfTnKO46jT6+nvTx9XQtM9nt7Csuc/r7HX7/L9JyMNkd4u+t04j2dvT4y10/HT3dVMhfI2GVEjc1GKtoApTQtyDcNM3Ze/cEggCHX3hfcbnbp5SdhSXMTs+hzCn+njqNXt4elVw/nTzcVXRIA2CxS7xUdSlFE6CEvoVj0ATRPp5E+3hyY7hjmdUu2V9Suec/N/04n9lzAPDQBD29HcJfHuffxVOJf12xSalcN4omQQn9eYheE3T39qC7twfXhQcCDhE6UGKq5POfd/Q4X6Q5gj3dNUF3L48Tcf4+HnT1cseoqiXVGDVhStFUKKFXAKATgq5e7nT1cufaVo5ldik5VGoivrCUHc4fgJ8zc5mdfgwAoxB093avFOffzcsdNyX+VaJSICiaCiX0imrRhKCTpzudPN25OiwAcIh/cqm5wiSvEhZl5fGNU/wNQtDNy90l/L18POjh5YG78k07wiuV+0vRBCihV9QKTQiiPN2I8nTjSqf4Syk5UmZmh3OwN76wlCXZ+czNOA6AXkBXL3dXuGeMjyfdvT3wPM/E3yIleqXziiZACb2izgghaO/hRnsPNy4P9Qcc4p9qslTq+S87VsD3Rx3irxPQ2fNEz7+3twc9fTzwasGFs612lI9e0SQooVc0CEII2robaetuZEKIP+AQ/3SThZ1On398YSkrjhfyw9FcwFGnspNT/Mtj/aO9PfDStwzxtygfvaKJUEKvaDSEELRxN9LG3ci4ED/X8qPOnn+5+K/JLWR+pkP8BdDJ080l/L2dmT3PRfG3qagbRROhhF7R5LRyM9DKzY9Lgk+If6bJ4hL++MIS1uYWnSL+FaN9or098Gnm4m9RaYoVTYQSekWzJMzNwCUniX9WRfEvKuGfvCJ+qiD+UR5up6R1bk7ib1UTphRNhBJ6xTlDqJuBi938uLiC+GebLewoPJHVc2N+MQuy8lzrqxJ/3yYSf5W9UtFUKKFXnNOEGA2MCTIwJsjXtSzbbKmU3mFzfjG/VBD/Dh5GV06gGB8Penl74Gdo2K+ClBKbyl6paCKU0CtaHCFGA6ODDIyuIP45Zqur1x9fWMqW/GIWVhD/yHLx9z6R4sG/HsW/PH206tErmgIl9IrzgmCjnlFBvoyqIP7HXOLv8PlvKyhmUQXxb+9upFIdXx8PAs5S/MuLxKgKU4qmQAm94rwlyKjnoiBfLjpJ/HcWlbjy+8QVlvBrdp5rfTt3Y4U4f8ePQE3E31reo1dRN4omQAm9QlGBIKOekYG+jAw8If7HLVZXRs/yqJ/F2fmu9W0rib9jwDfwJPG3OPP/Kx+9oilQQq9QnIFAg54RgT6MCPRxLct1in/FWP/fKoh/hLvBIfzO/D6t3AyAEnpF06CEXqE4CwIMeoYH+jC8gvjnVRT/olPFH9RgrKJpUEKvUNQT/gY9wwJ9GFZB/PMtVnYWlbKjsJTkUlOluwKForFQQq9QNCB+Bj1DA3wYGqAEXtF0nF8JwRUKheI8RAm9QqFQtHCU0CsUCkUL54xCL4RwF0JsEkLsEELsEkK87Fz+lhBirxAiXgixQAjhf5p96IQQ24UQi+vRdoVCoVDUgJr06E3AKCllDBALjBNCDAaWAdFSyt5AIvDMafbxELCnjrYqFAqF4iw4o9BLB0XOlwbnn5RSLpVSWp3LNwARVW0vhIgAJgCf1YO9CoVCoaglNfLRO10vcUAWsExKufGkJncAv1ez+XvAk4D9DMe4WwixRQixJTs7uyZmKRQKhaIG1EjopZQ2KWUsjl77QCFEdPk6IcRUwArMPXk7IcREIEtKubUGx/hEStlfStk/JCSkpvYrFAqF4gwI6cyqV+MNhHgRKJZSThdC3ArcC4yWUpZU0XYacDOOHwJ3wBf4WUo55QzHyAYO18qwsyMYyGmE4zR31HU4gboWDtR1cHAuXYf2Usoqe8lnFHohRAhgkVLmCSE8gKXAGzjE+x1ghJTyjL4WIcRI4HEp5cTa2d5wCCG2SCn7N7UdTY26DidQ18KBug4OWsp1qEkKhHBgthBCh8PV84OUcrEQ4gDgBiwTjkRNG6SU9wohWgOfSSkvbTCrFQqFQlFjzij0Usp4oE8VyztV0z4dOEXkpZQrgZW1tlChUCgUdeJ8nxn7SVMb0ExQ1+EE6lo4UNfBQYu4DrUejFUoFArFucX53qNXKBSKFo8SeoVCoWjhtDihF0Jc60y+ZhdC9K+wPEgIsUIIUSSEmFnNtouEEAnVrDMIIWYLIXYKIfYIIU6X26dZ0IDX4iYhRFyFP7sQIraBTqPONNR1cK7vLYRY79z/TiGEe0OcQ33RgJ+JSCFEaYXPxKyGOof6oCE/E8427Zz7eLy+bT8bWmKFqQTgauDjk5aXAc8D0c6/SgghrgaKTl5egWsBNyllLyGEJ7BbCPGdlDK5XqxuGBrkWkgp5+KcCS2E6AUslFLG1Y/JDUKDXAchhB6YA9wspdwhhAgCLPVldAPRUN8PgIPOGfTnAg15HQDepfq0MI1Oi+vRSyn3SCn3VbG8WEq5FscbWQkhhDfwKPDK6XYNeDm/3B6AGSioH6sbhga8FhW5AfiuToY2MA14HS4B4qWUO5z7OyaltNWT2Q1CI30mmj0NeR2EEFcCh4Bd9WNt3WlxQn+W/Bd4GzgljUMF5gPFQAZwBJgupTzeCLY1NjW5FhW5jmYu9GdJTa5DF0AKIf4UQmwTQjzZOKY1OjX9THRw1p1YJYQY1gh2NTZnvA5CCC/gKeDlxjKqJpyTrhshxF9AqypWTZVSLqzlvmKBTlLKR4QQkadpOhCwAa2BAGCNEOIvKeWh2hyvvmmia1HefhBQIqU8rb+yMWii66AHhgIDcHz5/xZCbJVS/l2b49U3TXQtMoB2UspjQoh+wC9CiJ5Syia7622i6/Ay8K6Uskg4MgY0C85JoZdSjqnH3V0A9BNCJOO4HqFCiJVSypEntbsR+ENKaQGyhBDrgP44btGajCa6FuVcTzPpzTfRdUgFVkkpcwCEEEuAvkCTCn1TXAsppQlHkSKklFuFEAdx3PFsqUdbakUTfSYGAdcIId4E/AG7EKJMSlnlwG5jcd67bqSUH0kpW0spI3H0zhKrEbYjwCjhwAsYDOxtPEsbnlpcC4QQGo4B6u8bz8LGoRbX4U+gtxDC0zl2MwLY3XiWNjw1vRZCiBDhyIeFECIK6EwTd4Lqk5peBynlMCllpLPde8BrTS3yAEgpW9QfcBWOnpYJyAT+rLAuGTiOY9Q8Fehx0raRQEKF15cD/3E+9wZ+xDHAsht4oqnPtamuhfP1SByJ7Jr8PJv4OkxxfiYSgDeb+lyb6loAk5zXYQewDbisqc+1qT4TFZa/hCNjb5Ofr0qBoFAoFC2c8951o1AoFC0dJfQKhULRwlFCr1AoFC0cJfQKhULRwlFCr1AoFC0cJfQKhULRwlFCr1AoFC2c/wcYV5fpJyBnUQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#clipPoly block 2 of 4\n",
    "#visualize the to-be-cut precincts, just to confirm there are really xx precincts up there\n",
    "for p in cutPrecinctList:\n",
    "        plotPoly(vtdGeom[p])\n",
    "        plt.text(vtdGeom[p].centroid.x, vtdGeom[p].centroid.y,p)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "e2595c62-579a-4af1-9552-0859524782be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#clipPoly block 3 of 4\n",
    "#now visualize the receivers\n",
    "for pp in range(len(precinctReceivers)):\n",
    "    p = precinctReceivers[pp]\n",
    "    plotPoly(vtdGeom[p])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "9e1d7fcd-cdeb-4317-9f89-5291a9595587",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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QSFxcHPfffz8pKSm88sor7Nq1q8NltY812zQl4m+6sSq9MWHVrqquqhYDS4Gm46z+Cbi9mUMuB24WkX3Aq8AMEXm53VG2U2JiYqNkv2HDhpCeT2tqKC85A/7RGo2fdboxJiIE0usmHahR1WIRiQdmAj8VkVxVrasu3wzsbHqsqj4BPOEvZzrwr6p6X5Bib1XPnj354he/yO9//3vWr1/PpZdeGrJziduNx+mkODWV1S++RGxCPBd97nM4O3BTN5o4/G03ao1a9bwKj7+5OdxhtEtX/aDuin9314zKYOaovkEvN5BeN5nAi/4ukg7gdVWdJyJvishwfG3v+/H3uBGRfsBcVb0+6NG2k9vtpk+fPuzdu5eKigoSEhJCdq6+KSkUAfP37gEgISWFYVdfHbLzdQV1N2Ot143PRf1TyOwZx5KC4+EOpd2kCzZNdrVOcKfKqtl/qiI8iV5VNwOXNLO+uaYa/H3pz0vyqroUX7NPp5o2bRo7d+7k/fff5+abbw7ZeWZ/+9sc37GDA5s2sWDfPmqrqkJ2rq6ivteNtdEDcPWovlwdgovYRIc5v18Zsg+nqH/cMyMjg4kTJ7JhwwYOHToUsvO44uLod8klpPrHw7HU1mAucKvRG9MmRUP2zSnqEz3A9OnTcblcbNq0KeTnErE7kHUc9sCUMe1iNfoLEBsbS0ZGBseOHQt3KN1KfRu9Nd0Y06ZQ1oe6RaJXVU6cOEFqamroT2Y3IOuJ0/fn5bUHpoxpk2I1+gtSXl5OVVUVffuG/kZYF7vRH1L1Y914LNEbEwhro78AhYWFAPTr16/Tzmk1enD4xw0SryfMkRgT+UKZM6I+0asqK1asIDk5uVMTvQHxJ3ob68aYwFjTTQft2LGDwsJCpk6d2qHhh9tLutpTGiFU3+vGEr0xbQplG0DUzjDl9XrZvHkz8+fPJyMjI6RDIDTHGm6sRm9Me4SytTcqE31hYSHvvvsuR48epV+/fsyZM6fzxpm3fvT1xOnvgeSxNnpjAhGqFoGoS/Rbt27lzTffJCkpidtvv53Ro0fbfK9h4nD6/rws0RvTNmu6CdAPf/hDVJX+/ftz3333ERcX1+kx1HWPsvr8uV439sCUMQFQDVn37KhJ9BUVFfXdk77whS/gckXNW+uypG6KKavRGxMQ63XThoSEBMaNGwfA/v37wxwN1kYPiMt/X8RuxhrTplBmjKhJ9ACzZs2iV69ezJs3j7KysrYPCIW6T2TL85QX++aPt143xrRNNXRP1kdV+4bb7eamm27ipZde4umnn2bIkCH079+f3r1707dvX3r16hXyfu7Wj/6c03sO0otz3SyNMa2zXjcBGjx4MF/+8pdZtWoVe/bs4dNPP63flpCQwNChQxk1ahQ5OTkhfYDKKvTnulf2Hj0szJEYE/lCOfVh1CV6gN69e3PjjTcCUFVVxcmTJzly5AgHDhxg165dbNmyheTkZG688UaGDx8ekhjEUr0xph2s6eYCxMbGkpWVRVZWFvn5+Xg8Hvbs2cMHH3zAn//8Z+677z5ycnKCdj5rujHGdJT1ugkSp9NJbm4uDz30EL169WLRokU2XroxJuxs4pEQiImJYdKkSRw/frxRO74xxoSP3YwNGq/Xy8qVK1myZAlOp5OUlJRwh2SM6eZsCIQg2rp1K4sWLaKkpIThw4czbdo0MjIywh2WMaabcwhU1YbmKfJul+hXrlxJSUkJt912GxdddJHdPDXGRISslHj2n6oISdndro3+qquuAuDIkSMhSfL1X79sCARjTDu4nII3RHmj2yX6IUOGkJ+fz8qVK1m1alXwT2DfEIwxHSASukTf7ZpuAK699lrKy8tZsGABLpeL/Pz8cIdkjOnmHCIhawjodjV68I2JM3v2bIYMGcLChQupqakJd0jGmG7OIVjTTbA5nU4GDBhATU1NSB6YUmujN8a0g0OEUM3R02aiF5E4EVkjIptEZJuI/NC//kkR2Swin4jIIhHp18yx2SKyRER2+I/9eijeREeoKtu3b6d///7ExsYGr2BrozfGdIAIeEKU6QOp0VcBM1T1YmAsMEtEJgI/U9UxqjoWmAd8r5lja4FvqupIYCLwZREZFZTIL9CxY8c4fvw4Y8aMCXcoxhjjb6MPU6JXn7pZPNz+H1XVkga7JdLMg12qekRVN/hflwI7gKwLjjoIdu3aBcDIkSNDUv6pg4dCUq4xJjqphm5QxIDa6EXEKSKfAMeBxaq62r/+KREpBO6l+Rp9wzIGAZcAq1vY/oiIrBORdSdOnAj8HXRQWloaAEVFRUEtt29uLgBnvTZPqjEmcKEcjz6gRK+qHn8TTX9gvIjk+dd/R1WzgVeAr7R0vIgkAW8CjzX5JtDwHM+oar6q5qenp7fzbbTfoEGDANizZ09Qy41PTUW8XkJ2V8UYE500QoYpVtViYCkwq8mmPwG3N3eMiLjxJflXVPWt9ocYGgkJCaSnp3PoUHCbWMTh/5WqDX1sjAmcEsZELyLpIpLifx0PzAR2ikhug91uBnY2c6wAzwI7VPV/ghJxkHi9XiorK4mLiwtuwf75Ua17pTGmvSSMwxRnAi+KiBPfB8PrqjpPRN4UkeGAF9gPfAnA381yrqpeD1wO3A9s8bfxA3xbVd8L8vtot+3bt1NaWkpubm7bO7dHXY3emm6MMe0Qysphm4leVTfju4nadH2zTTWqehi43v/6I0I3DeIFWbNmDampqYwePTqo5dbfNbemG2NMO4S16SZanThxgp49e+L0N7UEnTXdGGPaIZSTg3fbRN+7d2+qq6tDeIaI/CJjjIlQvhp9GPvRR5sDBw5QWFhIdnZ2uEMxxhjA10ZvNfogWrt2LXFxccyYMSNk57CGG2NMeyiErCGg2yX6srIyduzYwejRo4M7mFlT1nJjjGkPa6MPDq/Xy3vvvYfH42Hy5MnhDscYY+opGrI2+m4zw1R1dTWvv/46u3fv5uqrr64f68YYYyJBKHvddJtEv3TpUnbv3s2NN95oUwcaYyKS9aO/AFVVVaxfv568vDxL8saYiBTKR2+6RaLfuHEjVVVVTJo0KdyhGGNMsxQN2Vg3UZ/oVZXVq1eTnZ1NVlYnznliUwoaY9pBI2WY4q7o4MGDFBUVMW7cuHCHYowxLQrlszdRn+gLCgpwOByMGDEi3KEYY0yLwj6VYFe2b98+srKygj/uvDHGBJUNgdBhRUVFdMbUhE1ZC70xpj1C2UYf9f3oe/XqxbZt2ygvLychIYHs7OzQD39gjDHtZOPRX4AbbriB7OxsiouL+fTTT3nnnXd48803O+HMVqc3xgTON3qlDYHQIRkZGdx3332A7xf59NNP4/F4QnpOlVD9dxljopnV6INARMjKyuLQoUOUl5eH8kShK9sYE5Wse2UQTZkyhdraWl588UXKysqCXn79BL+W640x7WBTCQZRZmYm99xzD6dPn+bFF1+ktLQ03CEZY4x/4hHrRx80Q4YM4d577+XkyZO8+uqrQS1bvV6AkN1UMcZEL6vRB9ngwYPJzc2loqLivG0lJSVs27aNysrKMERmjDHBFfW9blrj8XjO609fVFTEM888w9mzZ4mNjeW2225j+PDh7S77bFUlx7ZuBZrcZGkwFqk2XFY9176v547qmZVFYp8+7T6/McbU6daJPisri+XLl3P48GH69euH1+vlrbfewuv1cvvtt7NixQpee+017rvvPoYMGRJwuaLKVq+XrW+8ccExJlRW8u//9V8XXI4xpvvq1ol+/PjxfPLJJzz33HNcfvnllJaWUlhYyK233spFF11ETk4Ozz33HH/5y1/44he/SK9evVotz+PxsGTJErSFGyoOEeJdLhJcLpLdbnrGxpIaF0es0+nbwX9c3cBG67dto8TRbVvXjDFB0q0TfVJSEg8//DALFizgww8/BGDSpEmMGTMGgPj4eO6++26eeeYZnn/+efLz8xk0aBCpqakkJibi9Cdor9fL2bNnWbRoEZs2bWL06NH07duXsrIyKioqqKiooLy8/Nzrs2c5cfZsfRxpaWnk5eXRs2dP4Fyi9xQUEFNWTvHbfwXgrfWFVNV6ye2T1LjZp56eayfyb6tbFLTR7tpgoWbpP3CdrQj6Hf+Ys8HvvmqMab9unegBevTowZw5cygqKsLj8dC7d+9G23v16sUDDzzA/PnzWbJkSaNtsbGxeDweamtr69dNnTqVGTNmtHg+VaWqqoozZ85QVFTEsWPH2LdvX/0HTSNuN/3LSjnyxBMAhHp+rD0DRwe1vLMxsezum83UcXlBLdcY0z7dPtHXSU1NbXFbZmYmDz30EGVlZRw6dIgzZ85QXl5OZWUlTqeTmJgYYmJiGDRoEP369Wv1PCJCXFwccXFx9O3blxEjRjBt2jTKysrweDz1NW1VRaurifefA+Bz//sRSfFufjb74oYl+ss9t1g/pnVdU9C5kzfqviVyrlmod3ZfRsbZQG/GRCNL9O2QlJTUoR44gZbdFlf//hwqq+bZ3ZWogtvp4J+nDyU92RK0MaZlbd7pE5E4EVkjIptEZJuI/NC//kkR2Swin4jIIhFptiorIrNEpEBEdovI48F+A93J2OwUyqpqeWfTYd7ZdJjnPt7Lh5+eCHdYxpgIF0iNvgqYoaplIuIGPhKR+cDPVPW7ACLyNeB7wJcaHigiTuA3wNXAQWCtiLyjqtuD+Sa6ix/dksePbvG1dx8vqWT8jz+gqja0I3EaY7q+NhO9+hqN67pPuP0/qqolDXZLpPnB18YDu1V1D4CIvArcAliiv0CxLl+7/Xfe3sqf1xzA44WUeDdzH8gnMdZa5Iwx5wSUEfw18/VADvAbVV3tX/8U8HngDHBlM4dmAYUNlg8CE1o4xyPAIwADBgwIMPzuq0e8i89PGkjh6QocInyw8zgAR86cJadPcpijM8ZEkoCexlFVj6qOBfoD40Ukz7/+O6qaDbwCfKWZQ5vrmN3ssMuq+oyq5qtqfjjmeO1qRIQf3ZLH8w+O59kvXMav7hpbv94YYxpq12OXqloMLAVmNdn0J+D2Zg45CGQ3WO4PHG7POU1gvP5umU5L9MZ0WaGafCSQXjfpIpLifx0PzAR2ikhug91uBnY2c/haIFdEBotIDHAX8M4FR23O4x8dGYclemO6pFBeuYG00WcCL/rb6R3A66o6T0TeFJHhgBfYj7/Hjb+b5VxVvV5Va0XkK8BCwAk8p6rbQvJOujmPv0ZvQ+MYY5oKpNfNZuCSZtY311SDqh4Grm+w/B7w3gXEaAJQ90St1eiNMU1Z/S9KePxNN06HJXpjTGOW6KNE3c1Yq9AbY5qyRB8l1HrdGGNaYIk+Sni81kZvjGmePSsfJfx5nh/8fRsxTgcX9e/J5ycNCmtMxpjIYIk+Sozu14OBaQms21dEcUU1C7cdtURvjAEs0UeNCUPS+PDffMMN/eCdbby14WCYIzLGRAprozfGmChniT5K2eBmxpg6luijUF1XS2OMAUv0Ucsq9MZ0QSGqpFmiN8aYCBDKypklemO6of+74m888NZ/svnI/nCHYjqBJfooZC30pi1zd/6cDaWv8d8fvxLuUEwnsEQfpayJ3rTGoTEAHK7YG+ZITGewB6aMiUJrC3fz+NKfgNZd4ur/pucbz9rjKAHgrKc4DNGZzmaJ3pgo9Oym1znuXQPqxuntge87Xt33PMGpPfA4KqmhIoxRms5iiT4KbDxQxC/f34WqIiJ8drws3CGZMEuJSQfgj9e+xdjMQc3uM/3FhynWPZ0YlQkXS/RdXOHpCm797QoAxmanoEDvpBguGdA3vIGZsHI5fJd2jae2xX3inIl4PWc7KyQTRpbou7hdx0sBuGpEH579wmVhjsZECrc/0Vd7alrcJ9mdjLf2LF6vF4fNKh/V7H+3i8vL6kmMy0F6cmy4QzERxOVwAlBd23KiH9AjGxEPHx/Y3llhmTCxRN/F9UmO467Lsnlzw0GOlVSGOxwTIepq9FWtNN3cnXcNAH/ZvrhTYjLhY4k+CszJz6bGo6zddzrcoZgI4Xa6gdbb6MdkDkDVwamzxZ0UlQkXS/RRILdvEgkxTn7/4WdU1XrCHY6JAG6nv43e23KiP1xajIiXZHdyZ4VlwsQSfRSIdTm5PKc3Ww+VsNu6VhrA7W+jb61G/+7OdQBc2m9Yp8RkWieEbvgSS/RRYPfxUhZvP8bkoWkM62u1M3Oujb61RP/engWo182c0TM6KywTJta9Mgp8esxXi/+PG0bhdtpnd1e2fO929p85hsfrRVXxqhdF8ahvue5fr3rxqNa/9qqi6sWLb3nHyd1Ay4n+xQ1L2F/9IVnu8aTEJ3XmWzRhYIk+ijgdNpRZV1ZYfIp//vAuRIL3BT4toed561Yf+JSfbf4XRON5cto3g3YuE7ks0RsTIcqqzyKiDI2dxc05V+MQByKC0+H/VxwI/n8d55Yd4sDpcOCo28d/TGp8crPDHzy9+s+IePj6mB8wPju389+o6XRtJnoRiQOWAbH+/d9Q1e+LyM+Am4Bq4DPgQVUtbub4fwEexnefYYt/P+vwbUwTMf6eMoN6DuKh/GtCdp7rc6awdfMbvL93BQ/nXxuy85jIEUiDbhUwQ1UvBsYCs0RkIrAYyFPVMcCnwBNNDxSRLOBrQL6q5gFO4K4gxW5MVHE7fT1lalu5gRoMW4/7xqAfkjIgpOcxkaPNRK8+dX323P4fVdVFqlr3F7kK6N9CES4gXkRcQAJw+AJjNk2EaD5h08nqhi3wqjek5xme3g+A5BjrodVdBNRGLyJOYD2QA/xGVVc32eUh4LWmx6nqIRH5OXAAOAssUtVFLZzjEeARgAEDrKbREYFOLrxw21FOlFZxvGoPRyoL6tt5HThAYFL/Mdw00gZIC4bDJaf553efojKAUSK96nvYrVZDW6OfNvBSfrkFjpUfDel5TOQIKNGrqgcYKyIpwNsikqeqWwFE5DtALXDe5JMikgrcAgwGioG/iMh9qvpyM+d4BngGID8/3+qoIXKyrIpH/7gegISBv8OZcP7k0O8d6MPkgX+tX3b4J6xIjIkjxmX379tjXsFq9lQvAk8Sou4293eQxmWZY0Ia056iIwDEuxNDeh4TOdp11apqsYgsBWYBW0XkAeBG4CrVZhsQZgJ7VfUEgIi8BUwGzkv0pnNU1fqaBf7jhpHMP51AvGsc37r0R3jw4vV6+eYHT3LStY7pf5l83rGu2n5s/KeFnR1yl+YSX3PMv1/yFPdfEhkPJtV4fN8c5h14ge1/3sEPpn2Z4b2zcYggAg4R/w9IoF8TTUQLpNdNOlDjT/Lx+JL3T0VkFvAtYJqqtjQf2QFgoogk4Gu6uQpYF5zQTUfUfR67YyopLNvHLUNv4aLMc01lP5ryb7yydX6jfQE+ObWCcuduZrz0MBMzJ/Hjq/+pwzH8899/wf7Sff6HfaB+PlNV/6u6+U3Vf//h3LLvtW/9KdYAkMrY+j18x2iTYxos+bedW9fSa388ov7V2jCq+n0b7V93bsF/40TxUA2u1p9Q7WzXDbuUt3c8yObTK/msajGfX7wQ9bpAHYATVYf/tQPUCTjI6zmd1+Z8P8yRm44KpEafCbzob6d3AK+r6jwR2Y2vy+Vi/6f+KlX9koj0A+aq6vWqulpE3gA24Gve2Yi/ecYE354TZXi853+xqquUCcLRkkrAy98PPU21p5rZw2Y32nfK4FFMGTzqvDKeXvFXXtjxG47rRuYfOMCP6ViiL606y0enX0C9sTg0ri7C+n+lyTKN1jRZ76ssU+zdhcvbq3EZ4t9H62qlTcuRBnuL/19HozX+ghA5t9ZX9LljpMm6c6VL/bGxjjyuyc3v0O8rFESEP3zuGwAs37eN17YupKSmyP+0rQev1uKl7rWHPWWfsKe86W0505W0mehVdTNwSTPrc1rY/zBwfYPl7wNWFQihOLev89SXXt5Qv86tML7SRV+Pg2NOLzUCsaqU9f6IHgM3UFBayOPjH2d4r+EBneOxyZ/jscmfY+qLD1Dq7fhNPI/X13Q0qded/OGWf+twOQBPr3+aZ7c+i9tdw++u+gHjM8dfUHnd0ZRBo5kyaHTr+zz/IOUUdlJEUOvxsvdkOTWec9/eVH3fnIb1Taa4oobjpZXEu52cOeubWEXx76PaeBmt//LVaF39a98x9dUj/3Y996XuvH3qv2WeK7q+XG10rvbdajxWUoXLGZqmMruzFgWmDkvn+Qcvo6rm3BDFxxYdouzTEtwpMQwtrsaLh2VDX2N3n9VkOAZx58Xf4J4R93TofDWOY4x99qoma5v+gUqzS4qCK/AeQq15bNxjHCs/xry98/jqP77K6nut1hkK8a4kimuLWVe4l/zswSE5R63Hy6o9p3l3yxEWbTvKqfLqkJwn0k0dlh6Sci3RRwG308GVw/s0Wvfe8hPUJLp46KnJlFVVcPlfJgEwwJPLb2f9moF9szp0rjnDZ/PWp+76ZhOfxjUXpWlNpvGyMIi7RgfnicyfTP0J8/bOo6K2gvf2vMfVA6+un3TDBMd3rvgiX1m6kocX/jPz7niV/j1TglJujcfLis9O8d7mIyzafpSiihoSYpxcNbIv04elkxjrqr85LMDjb23mZFnjD4DHZuYybmAq4G9MkwZVDDl/nUjTZf+RTfeBRk2e9a+brDvXrEd9GdIggPZWaPr1jG/fAQGS9n696Az5+fm6bp3ds70QO1Yc5h8v7eTu702gkM+4b/G99dtSa/qw7OEPwhhdcD216ileLXgVAIc4GJ8xnl9M+wU9YnuEObLo8fyG+fxi8+Pkxs/k7Tt/0eFyqmu9fLz7JO9tOcKi7cc4c7aGpFgXM0f24bqLMpk2LJ04t7PNcrxe9Sda6xVUR0TWq2qzN4OsRh+lThaW4XAKcUluLu4xho/u/Ijtu/fwyPrPMyz2/JutXdm3LvsWGYkZfFb8GcsOLmPVkVXc/e7dvPO5d3A62k4apm0PXnodT6//v5xxHm/3sZU1Hj7adZL3th5h8fZjlFbWkhzn4upRfbk+L5MrcnsHlNwbcthIre1iiT4KVZ+tZcfKI+Tk9yGhRwwAPeN61j95WfdvtHA5XfzTRed6Ac16cxYHSg8w4y8z+PEVP+byrMvDGF30EEctBPDQF/iS+4efnmD+liO8v+M4ZVW19Ix3M2t0BtdflMnknDRiXfYh3Fks0Ueh/VtPUVPpYdTkfo3WTxg1lkvWT2Yty/l/777MozfcF6YIQ+vdW9/lmx9+kw8OfMCX3v8St+fezg8m/yDcYXV9UgPacso4W+1hacFx3tt6lH/sOEZ5tYeUBDc3XJTJ9WMymTQkjRiXTYwTDpboo1CP3r4bOkVHy8kanlq/3uV08f/ueZo7X/48vz3+M2L/4eYLM+4MV5gh43Q4efrKp/n09Kfc/vfbeXPXm3i8Hp684slwh9alqdSgTWr05VW1LCk4zvwtR/nHzuOcrfGQlhjDzWOzuOGiTCYM6WWznkUAS/RRqM+gZNL6J7H94yPkTWs8qGh8TDwv3TmX+/70EP9z4CkSV8Rzx+SbwxRpaP1q46/qX39Q+AFPYom+I85UnWHuxtfxOko4yTIW7zjEkeIaPt59kqUFJ6iq9dI7KZbbx2Vx/UWZjB/UC5cl94hiiT4KiQiD8tJYv2A/Ho8XZ5OLLiWxJy/NeY47X7uXn2//KRNz8snu06+F0rqm0upSlh1cBsBvZvyGsX3HhjegLuy7H3+XJYVLAFB18MWX1oG66JMcy12XZXP9RZnkD+plU1lGMEv0Uaim2sPezSfpkR5/XpKv06tnT753+Xf5yppHuendG5joupKf3vpDeiZF1xjlkzInMTV7arjD6NIqaiuIccTw2vXz2FboJX1KLEPTk+iTHGvdG7sI+34VZWprPCx+dhunj5Qz9c5hre47ZfQE5k5+kfGOaXzsXcwv5/+2k6IMPad/1MjzH94y7SUII9NGkpOWyS1js5g8tDd9e8RZku9CLNFHkVO7i3n9sWXs3XSSKXNyGZiX1uYxlw2/mGceeJqBZ4ezqGQeZ6uiYzpfh/9P2xL9hXOIw36PXZwl+ihQW1xF2YrDnJm7hanJLkZnJTLmyux2lXHv8PspdRUz9x/RMVWA+NuLI/HJ765GEPs9dnHWRt/FqSpH/8s3Lnt8rziqT51laFkVO1/azvD7Rwb89fq2y6/jl3v+i5WHV/FVHg5lyJ3CYXWYRrzq5WDpwfqaecMhlmkwfgucG1ZAEDzq4WztWUv0XZwl+mjgFPAoGd8cx9lTlez/7SaSt59i65OrGfbYpcT6n45tTWxMDNkyhC2ONXzxpa/xg+u+TVZ6RicEHzyff+tJPinyTUms4gUnLN8Wx5DlvumMVc+NGV//r3+dqouJI0t49fPR+RDZ3C1z+fXGX3f4+Ev7XBrEaExns0TfxYkISZdnUb7yMOJ0kNAngeHfHs/u57eRsqeY3f+znmHfHIc7ue1k/4vrfspPF/2Sj3QR355Xw4sP/q4T3kHwFJzZiIdaBsSNQxDKqiuITczFnVANok1GJVTqpwcRZdPuND47XhPW+EOpqLIIgJ9M+Umj2nnjmbfOXxYRElwJjO7d+pj1JrJZoo8C4hTU461fdsQ4GfboGHY+v5XknafZ/fN15Dx+Ge741scpGZTZn9898AtunXs3G1wf81/v/IrpuVOYODIya3NlVbWcKCvF7fJN4Vfr9ZIoGcy/93/bXdbo/3yx6WjKEaesuoxNJzadl4zrtHbD9GDZQZLdydw45MaQxmgikyX6KCBOAS+oV+tvQgKMeDCPXX/eSfKmE+x9/VOGPRBYrWxaxpV8dnI7rxTN5dVVL/D1g4/z4NWRN1TCFf/7Szy9Xzq3wgUJ3ta7lNaprq3l4JlTFJac4kjpSSo5QZyj7W894fS7Tb/jpe0vtb1jCzITM4MYjelKLNFHgdJKD5srPGz+13kkFa5hQHw5lY4DlPTcjOPacZTmplF54Ar6bKmk58gxiKv1mv1jNz7Co+UPcLjoGF9b8Bj/c/g/SVqSxB1X3tBJ7ygwA/pUstcLl/W4jwR3HIIwK3fyefsdLD7Dwl3rWXVoM7uLd3K6dg8e1zFEzn0LSsgGT/UQ4Gud+A7ap9ZbS7wrnj9c84f6dfU3VJu+lvPX903s2wlRmkhkiT4KLFp6iIpqL1Qn4k4eR58NP0YrS0nGyfFxa6kdrFRXCwf/+CY94t/nZPx4avtPIfHiq0gedRniPP/PID4xlqGJA3j13pe44ZWb+c99/8HmNzfzyJQvkN0nMmqG2alJ7D0Fa0//jRuyH6RPQm/iXXH8ceMSVh7cREHRDk5W1yV1X7OGQ5NJiRlMVsJE0hN60ys+lT4JvZhb8CRxiaeCHmOVp4q/7f4bFTUVePH65x7V+n+96q2f17Ruu1d9H0BNt286sQlV5eL0i4Mep4luNsNUFPj7rzdxYNspMit3cdKRwSPP3cKhv/2Kkm/9Hu+EdI7df5DsYxnk7toGQLG3PymOgwBUaSKlzsHUulPwuHriFC/iOYsjLhEGXoGmDaVg1zqeKZ/HJz3KcCjc6MrhqfveDudbBuCzU0f5+YqXWX7iNcR5/oNe4ulBqmswQ3uMYELWGK7JyWdQSmazXU4vfulivOrl+WufJz+j2Ul6OmTF4RU8uvjRdh3jEId/qjqp7wbpEAciwrDUYbx8fXQ862CCy2aYinL9h6dyYNspjsTl4q4pY+/sO/D6n3Ctqj6GOqQ+yTPkSnre/zZn9uyhdOMHyP6PiKnYT1zVIdyVO/CqC4/E4i4rJbHoXQD6ABM0luVnMngsW1lfVRCmd9rY0LQMfnfTv7Lr5H3sPLmf9/espriylHGZeczKyWdYeuDz4tbVout6pwRLXbl/uOYPjOk9BhFpNZEbEwqW6KPAgNG9OLwrjdrTRSQd3IszrRdOwNPLi2taLL1PrqEswUlShQf2LEF2vkfPkTfQc+hQ4JFmy6ytquXk1jVI2TGc6YPpmTuCq9xxZDyXRx9nHOr1Io7IeCgpt3cGub0zuGnEhA6X0T+pP4fLD3P1oKvb3HfHqR28VvAatd5aoHFvl6bdFE9UnAAg1hlLgjuhw/EZcyEs0UeBtKwkbvhyXbvt9PO2/+LOG3kfGJlWwrXpm3G8di/c+QoysuWbq65YF73HnX9jc3bqRfxvyVbeeP8b3HHN00GJPxLUeGvqB0Jrzr4z+6iorQDgp2t+yobjG8hIzDj3NGmTp0rrCMKIXiPon9R4XgBjOpMl+m5CEb5xzffo4yrjrU1f5+jCH5PXSqJvTnVVKZtK9wOw7fR27ghFoGHSWqLfcWoHc+bNabSud3xvFs9e3BmhGXPBLNF3Aw6ni6Ezr6e4Z2+oTWRrUg6DK4+2q4yCXe/x+EdPsNvh5aG4QTx60wuhCbaTbDmxhV9v/LWvDV18sygpysMLH0Zp3DOmtLoUgP8z9v8wInUEAAN7DAxn+Ma0iyX6bmLn/n0weDy9nS5GlO8lo/oUun+lr6mhac+rPiMgPrXRqvd3vMpuh5cHk0fwL7f9pfMCD4KDpQc5XnGcWm8ttVqLx+vhvb3vsfLISsamj0VE6pthqr3VjZphBKFnbE+m95/OnGFzSItve+hnYyKNJfpuwBUTw5GTvpuCu8VNkbsHGdWnkOdnNX/A8Bvg7j81WnWi/AgAd1761ZDGGmzVnmpu+estVHurz9uW4Erg+VnP43LYZWCim/Wj7wZOHSrkzPHjbMNFTNYAepw9xkXle0iKjWuwl/8m4gc/gmNbIalu5EoFVd72FvG99DTiHDH0jEttegpfCS10D2z4xGag24LV1VBVOVx+mDuG3cF1g6/DKU5cDhdOh5M+8X1IT0gPynmMCTfrR9/NpWVlk5aVzZD6NanAiOZ39tTA1jcA8Q/z6Pt3lnr5tOYQ5RmjwXn+mDAtDajVWkWiI7MWqWqbHwJNzzneMZ4HRj9g7eqm22oz0YtIHLAMiPXv/4aqfl9EfgbcBFQDnwEPqmpxM8enAHOBPHzjAz6kqiuD9QZMkA27xvfTRDzwrc6PxhgTBIE88VIFzFDVi4GxwCwRmQgsBvJUdQzwKfBEC8f/CligqiOAi4EdFxy1McaYgLWZ6NWnzL/o9v+oqi5S1Vr/+lXAeU+EiEgPYCrwrL+s6uZq/cYYY0InoGfYRcQpIp8Ax4HFqrq6yS4PAfObOXQIcAJ4XkQ2ishcEUls4RyPiMg6EVl34sSJwN+BMcaYVgWU6FXVo6pj8dXax4tIXt02EfkOUAu80syhLuBS4HeqeglQDjzewjmeUdV8Vc1PT7eeEMYYEyztGpXK3+yyFJgFICIPADcC92rz3SsOAgcbfAN4A1/iN8YY00naTPQiku7vOYOIxAMzgZ0iMgtfR4ybVbWiuWNV9ShQKCLD/auuArYHI3BjjDGBCaQffSbwoog48X0wvK6q80RkN74ul4v9/ZpXqeqXRKQfMFdVr/cf/1XgFRGJAfYADwb9XRhjjGlRm4leVTcDlzSzPqeF/Q8D1zdY/gQI3pQ9xhhj2iUih0AQkVIgMqYxaltv4GS4g2iHrhRvV4oVLN5Q6kqxQnjiHaiqzfZkidQhEApaGrMh0ojIuq4SK3SteLtSrGDxhlJXihUiL97ImAvOGGNMyFiiN8aYKBepif6ZcAfQDl0pVuha8XalWMHiDaWuFCtEWLwReTPWGGNM8ERqjd4YY0yQWKI3xpgo12mJXkTuEJFtIuIVkfwG69NEZImIlInI/7Zw7DsisrWFbW4ReVFEtojIDhFpaVz8iIjXv32MiKz0l7/FP7lLRMbq32eAv4x/vZA4Qx2viFwtIuv9v9P1IjIjkuP1b39CRHaLSIGIXBuOWEVkgYhs8h/3e/9T8E3LjZjrLJB4/fuF/ToLNFb/vkG9zhrqzBr9VuA2fLNVNVQJfBdo9s2JyG1AWXPb/O4AYlX1ImAc8KiIDLrgaEMUr4i4gJeBL6nqaGA6UBOJsTbwS5ofhrqjQhXvSeAm/9/CA8AfLzxUIHR/C6OAu4DR+AYK/G1riSCEsc7xTyyUB6Tju6aaiqTrrM14I+g6C+R3WyfY11m9Tkv0qrpDVc972lVVy1X1I3y/rEZEJAn4BvCfrRUNJPr/Y+PxTW1YEsHxXgNsVtVN/vJOqaonQmNFRD6Hb4yibRcSY5O4QhKvqm70D8GBP944EYmN1HiBW4BXVbVKVfcCu4HxnR2rqtZdLy4gBpqdzDdirrMA442I6yzAWENynTUU6W30TwK/AJodHdPvDXzj3B8BDgA/V9XTnRBbcwKJdxigIrJQRDaIyL93TmjnaTNW8U0S8y3gh50VVCsC+d02dDuwUVWrQhdSqwKJNwsobLB80L+u04nIQnwTC5Xiu6aaiqTrLJB4I+U6azPWzrjOgjoEgoi8D2Q0s+k7qvq3dpY1FshR1X9p4yvieMAD9ANSgeUi8r6q7onQeF3AFcBl+JLAByKyXlU/iMBYfwj8UlXLxDdCaXvOEY546/YfDfwUX60u0HOEI97mfqlt9ncOZqz1J1W91t+G/QowA9+c0A1FxHXWjnjDfp21I9YOX2eBCmqiV9WZQSxuEjBORPbhi7OPiCxV1elN9rsH3+TjNcBxEfkY32iZbf4Bhineg8CHqnoSQETewzcZS6t/gGGKdQIwW0T+G0gBvCJSqarN3niMgHgRkf7A28DnVfWzQE8Qxr+F7AbL/YHDtCHIsTYst1JE3sHXpNQ0GUXKddaw3NbijYTrLNBYO3ydBSpim25U9Xeq2k9VB+H7ZP60uQsb39fIGeKTCEwEdnZepD7tiHchMEZEEvztndPo5MlYAo1VVaeo6iD/fk8DPw7mH1+gAo1XfBPkvAs8oaofd2qQDbTjb+Ed4C4RiRWRwUAusKbzIvXdSxCRTP9rF74hxpu7fiLiOmtHvGG/zgKNtVOuM1XtlB/gVnyfslXAMWBhg237gNP4eigcBEY1OXYQsLXB8s3Aj/yvk4C/4LuJsR34t0iO1798nz/ercB/R3KsDdb/APjXSP7dAv+Brx35kwY/fSI1Xv/yd4DP8A3LfV1nxwr0BdYCm/1/k78GXJF6nQUabyRcZ+2JNRTXWcMfGwLBGGOiXMQ23RhjjAkOS/TGGBPlLNEbY0yUs0RvjDFRzhK9McZEOUv0xhgT5SzRG2NMlPv/pdz6h88z8RoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#now visualize the TRACT receivers\n",
    "for tt in range(len(tractReceivers)):\n",
    "    t = tractReceivers[tt]\n",
    "    if notPoly[t]==1:\n",
    "        for geom in tractGeom[t].geoms:\n",
    "            x,y = geom.exterior.xy\n",
    "            plt.plot(x,y)\n",
    "    else:\n",
    "        x,y = tractGeom[t].exterior.xy\n",
    "        plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "d2622568-1fc9-4089-9ae4-2550ea6fbaaf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I have finished slicing  170446.0 people (VAP of 124554.0 ) and  56675.0 voters out.\n"
     ]
    }
   ],
   "source": [
    "#now, cut and redistribute\n",
    "cutPop = 0.\n",
    "cutVAP = 0.\n",
    "cutHisp = 0.\n",
    "cutBlack = 0.\n",
    "cutTrump = 0.\n",
    "cutBiden = 0.\n",
    "\n",
    "for ct in range(len(cutTractList)):\n",
    "    t = cutTractList[ct]\n",
    "    cutPop += tractPop[t]  #sum up the cut pops\n",
    "    cutVAP += tractVAP[t]\n",
    "    cutHisp += tractHisp[t]\n",
    "    cutBlack += tractBlack[t]\n",
    "    # Now we zero out the pops in the cut tracts\n",
    "    tractPop[t] = 0\n",
    "    tractVAP[t] = 0\n",
    "    tractHisp[t] = 0\n",
    "    tractBlack[t] = 0\n",
    "# Now distribute the pops among the receivers\n",
    "ntR = len(tractReceivers)\n",
    "NTR = float(ntR)\n",
    "for rt in range(ntR) :\n",
    "    t = tractReceivers[rt]\n",
    "    tractPop[t] += cutPop/NTR\n",
    "    tractVAP[t] += cutVAP/NTR\n",
    "    tractHisp[t] += cutHisp/NTR\n",
    "    tractBlack[t] += cutBlack/NTR\n",
    "\n",
    "#             now do the same for the precincts\n",
    "for cp in range(len(cutPrecinctList)):\n",
    "    p = cutPrecinctList[cp]\n",
    "    cutTrump += vtdTrump[p]\n",
    "    cutBiden += vtdBiden[p]\n",
    "        # Now we zero out the pops in the cut precincts\n",
    "    vtdTrump[p] = 0\n",
    "    vtdBiden[p] = 0\n",
    "\n",
    "# Now distribute the pops among the receivers\n",
    "npR = len(precinctReceivers)\n",
    "NPR = float(npR)\n",
    "for rp in range(npR) :\n",
    "    p = precinctReceivers[rp]\n",
    "    vtdTrump[p] += cutTrump/NPR\n",
    "    vtdBiden[p] += cutBiden/NPR\n",
    "print(\"I have finished slicing \",cutPop,\"people (VAP of\",cutVAP,\") and \",cutBiden+cutTrump,\"voters out.\" )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "fe47f683-719d-4a02-9648-905bddbf28a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#now work on the west ear\n",
    "plotPoly(tractMAP)\n",
    "pt1 = Point(-115,34.3)\n",
    "pt2 = Point(-114.1,34.3)\n",
    "pt3 = Point(-114.1, 36.2)\n",
    "pt4 = Point(-115,36.3)\n",
    "clipPoly = Polygon([pt1,pt2,pt3,pt4])\n",
    "plotPoly(clipPoly)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "cac3531a-35c7-438d-b5b9-5fcdf0745329",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I have flagged  47 tracts w pop 141628.0  to reassign to tracts...\n",
      "[335, 336, 774, 1037, 1038, 1579, 1580, 1645, 1651, 1653, 1719, 1723, 1743, 1745, 1747, 1748, 1750]\n",
      "I have flagged  17 precincts to reassign to precincts...\n",
      "[102, 112, 114, 116, 187, 277, 346, 347, 351, 1474]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#find all tracts and precincts centered in this clipPoly, flag for cutting\n",
    "# and, ID all nearby tracts and precincts to receive their pops and voters\n",
    "cutTractList = [-99999]\n",
    "cutPrecinctList = [-99999]\n",
    "tractReceivers = [-88888]\n",
    "precinctReceivers = [-88888]\n",
    "popToCut = 0.\n",
    "for t in range(nTracts):\n",
    "    x = tractGeom[t].centroid.x\n",
    "    y = tractGeom[t].centroid.y\n",
    "    if tractGeom[t].intersects(clipPoly) :\n",
    "        CP = Point(x,y)\n",
    "        if CP.intersects(clipPoly):\n",
    "            isSkippedTract[t] = 1\n",
    "            popToCut += tractPop[t]\n",
    "            if cutTractList == [-99999]:\n",
    "                cutTractList = [t]\n",
    "            else:\n",
    "                cutTractList.append(t)\n",
    "            plotPoly(tractGeom[t])\n",
    "    else:\n",
    "        if tractGeom[t].distance(clipPoly) < 0.6 and tractGeom[t].centroid.x > -114.2:\n",
    "            if tractReceivers == [-88888]:\n",
    "                tractReceivers = [t]\n",
    "            else:\n",
    "                tractReceivers.append(t)\n",
    "xp,yp = clipPoly.exterior.xy\n",
    "plt.plot(xp,yp)\n",
    "\n",
    "for p in range(nPrecincts):\n",
    "    x = vtdGeom[p].centroid.x\n",
    "    y = vtdGeom[p].centroid.y\n",
    "    if vtdGeom[p].intersects(clipPoly) :\n",
    "        isSkippedPrecinct[p] = 1\n",
    "        if cutPrecinctList == [-99999]:\n",
    "            cutPrecinctList = [p]\n",
    "        else:\n",
    "            cutPrecinctList.append(p)\n",
    "    else:\n",
    "        if vtdGeom[p].distance(clipPoly) < 0.6  and vtdGeom[p].centroid.x > -114.2 :\n",
    "            if precinctReceivers == [-88888]:\n",
    "                precinctReceivers = [p]\n",
    "            else:\n",
    "                precinctReceivers.append(p)\n",
    "print(\"I have flagged \",len(cutTractList),\"tracts w pop\",popToCut,\" to reassign to tracts...\")\n",
    "print(tractReceivers)\n",
    "print(\"I have flagged \",len(cutPrecinctList),\"precincts to reassign to precincts...\")\n",
    "print(precinctReceivers)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "d7009c9a-d5ff-4570-855d-1fb439afc32c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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LvGTcNZzQtlr8y8vxryyncfE+9BQHngk5uM/IPOjHoYgbwu0CGpCBLjZQIRZVfRjdFPVU6aQITeDok4yjTzJGqDeB9VU0LtxL3cxi6mYWA5Bybb9WyZetODWs/QcDX+JbEcJ1cQxh6SIPWSMKmhol1R1RTVKdnFh9CN9XpQRWVxBrCB+2rXHZscJ7KdoLYbXgSNxHJJxDZM2qeJvTeqgmqW6L+tY7IYY/QtXLm4iUNGD4owBY0pzY8jxYRmRgTXdiSXdhzWxe/CNF25F41RmEntpO5at1pDtsWAcMjrdJp48Kb95tUd96JyRaFTyQI8M5PB3vtAKsGUocOiLW3n1Jv95P+QsNVL2wjszfDURonbxib6g+jO5KJ//ldk+seR6SLu0FQHB9NXqiLc4WKU6EbchwPHnbiUZzqPvXC0ijkw+FVk1S3RYlGJ2QWF2I8O4GAGw9EhBW9bbX0Un89g14covx7Smk7vHnOrdoqFhS3RYlGJ0M/5pK9j20BP/ycjwTckj75mCEpmKgd3SErpH4nRtx5+zAt7snNY++iAyHT16wo2EYIA0lGN0UJRidDBk95M3UoiEjR+aOVnRUhKaRdPf1JPTYir+sB5UPv0ps3954m3Viwo1QuwsCNWa4dtn0e9OVYHRH1LfeyXCPyMDew0vte9vwfVGC7rWRMCk33mYpmonQNBK//U0sr79JzdJMqv/+Jp6Ur3BcejOizznta0wsApVbzCYmVxokHvE7qtkJL34dKjYcXOfJMj9VDaNbor71ToglxYFzcBrBDdXYe3SxsBPdAKOmGj26jUzbs1i1XeADXngP7Ikw5AqY+ou2ObGUsG81bJgJWz6EsnWmWBzKxY+a+5WugFUvQywEvadDr2ngr4LqYkBCvwvbxkZFh0YJRidF6Ga/Rc3bW0m/fQiaXX2VnYHI2qVor1+Ogzoitn6EBv4B6/BRaPP/ANu/gKVPm5P1enDlQjQCllbyqn7iTNi7CoQG+eNg/F2QOQQay2FOk0i99z3z0+aBQV+Ds39zdM1D0W1RT5lOinNYOry8icgeH+FdDTj6JMfbJMVJCJUWY339YiQOIhe/g/WMqQc39nzLjNG0+ElY8iRU+6FuFzyQDgnZkDsS+l9kPsStjuaftGob7Jxv1ir2rgJvLtzxBbiPyKY85g7wV5od2lKCOx0sari24nCUYHRShBC4RmXiX1qGvXdSvM1RnATDMKj67/fJwQ/4Kf34enwLUol6c9BTepI67DoyCybB+G+b0/89DRXbwZoPdXtg40xzevtOcKZA1hDoNwOGfR2ch7ws+Cpg3yrY9hls/gCqtprrE7Jh0g9hwv8D1zHyqOgWSMhqvQuWEoQavdfVUILRSYn5woS21GLNdiPUjdnhWTb3C/Iab2ZZnpeIuxRbw14SGyvIq95FwvavCCx/kXXnP8CgsXebBXQrJBXAbWsO9imsfsVstqreBts/N6cPfmo2HzmTIVgPobqm8jYonAxjboeeZ0Fqb2hrD/OGMghUmx3ljRUw7BoYeyek92vb8yraDSUYnRAZk1S/uJFYY4TUGwfE2xzFSVi3ehnOT/1sdxmMuvNpbIc09YRiIbbsmo/npWtwzv0D7BeMQxHCbJLKHXlwXfUOWPk8bP0EKjdBXQnQlA8lqcAUi6QCc7l0BdTsAFeq2RTlTgObu/UusHILzPwB7Pjy4Dp3Oqx4weyP6TUNxn4bep/T9qKlaFOUYHRCwiUNhIrrsOUnYElpQXu2ot2IxCKU15dRPHcVeYtd1FljZF07+DCxALDrdvoUTWOWw8nIxobmnyClEKb9ypwAIkEoXQ47F8Cur2DTbNN34njkj4MrnjwoKqfDs5dC/Z7D1930DngyYdn/YMl/4cWrzFrOmDtg+LVgTzj98yraHSUYnRBbrgfHoFSC66oofWAhtrwE7L2TcI3MxJrmjLd57UYsHKG+tpb62lp8dXX4632EfH7CkTAx3cCwSLAINJuOxWbDYrNitdux2W3Y7A5sDjtOuxObw4HT6sShO7Bq1uM28QWjQUorS6ivqcFf10Co3k+0IYThi6D5JXoAbAEdZ8hGQtSNy3BQhJcNaTsZdcsFJCenHvdaqoiReDrhQqwO6DHBnA78g6IQrDOFI1BjDottrID6UljwD/jPFLj8KThd/49xd0HxZ6YIBOtg26fwye8gxYx3xiX/hGAtLHwc3v8xfHo/jLjBbC5LKTq9cyvaFSUYnRBh0Ui7cSDhPT4C6yoJba2l4dPd+ObtIeunY9DdnS+5jYwYBBoaqaupxldfR2NdPaGGAJHGILHGCCJgoAXAFrLgCFtxRR04DPNtXQcSgUR0oDlvruGmySQCNIgwYS1MWEQIa1EiWpSoHsPQJLrUSQy58Ebd2KQVc3yRtemsYGDgs/jx2YKEHBEavBEa3T6sCVEcWV7OnHA1Nv3EI4765k3AtX4OoWAtdkdSC/97x0G3gDvVnI5kyJXw6k3wwpXmA3/KPcfuDD+SWAQ++a0pOuO+Y64rGA+jbzVFy18NfywyO9z3U7ocvvWBec6SpbDo37D4CVNA+l1g9nMUTVGd5J0AJRidGFuuB1uuh8bkfYR3NaB77Wj2UwtE6GtooGJfKdUVFQTqfeg2C1anHZvDgd3pwOFy4XS5cbnduBxuNHH8tmgZNYg2hmmoq6W+tobGeh/B+kbCvgCxxjD4Y2hBsAY17GErrogdh2E/UN7ZNIEDcNCg+Wm0BAjYQtTbQlR7DQynQHNZ0d1W7B4XTq8bT1IiXm8SHocHERUQNYiGI4SCQSKhMOFQkEgoQjQcJhqOEAtFiEWiGJEYRjgGEYmMGoiIgKgVa8wKMYkwoDrRT7U3jCvRi93rxJnowZOcSGJyCi5vApp+em3z7rQBwByKN7zFgBHfPK1jNYvUXnDLRzDn57DwMVj+LIz7Noz/DjiTjl9u4eNm7QRg7RsH14+9Ey54GEL1R5c59LeSNwrynoLp98PS/8LS/5nNZxkDYewdMPTrYO0+teTOhhKMTk6kwk/NG1uwZDhJv2UIwnL0gysajFBdXk5VWRkNVTUEq30YdRF0n8Tpt+INmc0nViATgEM7RINNU+2B9/IKESGohQnqYUJ6hIgligUL7ogDZ8SOM3bw4W/h0Ld/D41agAZLI35LiEZ7hIjbfPjj1NA9NmweB44EN26vF29SEklJqWQ7E9G7eP6FXmPvpnHe3wkuegzaQzAAbC7Ts3vsnTD3D/DFH2Hxf2D8/4Nxdx7sZzAMc/RT1Tb4/GGz/2Pyjw4e5927zc7t1a+YyZUAzvwp5I4y5zMHHn1ub7bZ/zL5Hlj7Oiz8t+k0+PF9cMbNMPo25TDYAVGC0cmJYt6g0fIAu/6wgJiIEdVixISBIQxshhV3zHxjSwASsAEp1Fjqqbf7qfP4qc4OoCXacKZ4SEhLITE5mUgoTCgQIBwIEg6GiAbMN3IjGEWGYxCWiLBEhEGP6ISIUu0sJ+oAXBqay4LV48DhdeFKSMCTlEhSUioF7mScFvUGeSQudzorMnqTW7m9/U+eMQCufhb2rjaF47MHzFpEznCz76N8w8EQIhYnfO3fh/c9nP8H2LXw4LLVaTZznaimcmBfh9mfMfx6s8N+0eMw/1GY/3cYeIk5uip/jGqu6iAowejk2FPdzB+2iR17tmKRFixSJ9HiJc+VCwZERBSZoGFLceFJTSIlPYPMrBxynYnKf6ODIWweLDKOeTKyh8K1L8Ge5eZDe/3b5vqxd0JKT9OxL3s4JPc4vNzgK8zpdBACCieaU81O09t9+bOw7i3IGWEKx6CvKe/zOKMEo5OjaRpfv/ZWpJQsLVvKg4seZGvtVi7tdSmX9LqEEZkjsGqdrxO8OyJ0GxYpkVLG15DckXD1M7D6NfCVwYRj+Ia0Jck94NwHYOrPYdVLsOg/8Nbt8NGvYdS3zMmT0b42KQCVD6PLIIRgdNZoXr34VW4edDPvbHuHWz68hWtmXkNloDLe5imagbQ6cRoGgeAJ/Cfak6FXtb9YHIrNbY6+umsR3PAGZA8zm8z+OgjeuhNKV8bPtm6KEowuhlWz8qNRP+KTqz7h/on3s7lmMx/u+DDeZimagbXPuViBPY+PIRrxx9ucjoOmmV7i178Gdy8zO8XXv2tG3336fLPZKhaNt5XdAiUYXZBANEBVoIqV5SsBSHE0Y3y9Iu4MGn0XiyfcTl5DJXL3QsIn8tTurqT1hhmPwI82wHkPmiFRXrsZ7k+FlS8eHKWlaBNUH0YXoqyxjK+98zUaIgdDTHxz8Dc5r/C8OFqlaAljzn2EjXljMV6bhbV6G7uLPya/Zztn4usMOBJNn5HRt8KTZ0PZGnj72+Y06hbTpyStT7yt7HKoGkYXIhgLHiYWT537FD8844dqNFQno//AKxE5IxFAyarn4m1Ox8Zih2/Pg3tr4cI/mz4iK56Hf46CF64yw5TEexBBF0IJRheih7cHq25axT+m/QOnxcnrm1+Pt0mKU8Rm8wAQiwbjbEknQQiztnHLHPjBOjPNbelKeO5r8Nh4WPYMRALxtrLTowSji6EJjan5U5maN5V5e+bF2xzFqSJMz3Z73R7VLt9SPOkw9afwg7Vw2b/NmFrvfRf+MhA+uR/q98bbwk6LEowuyuC0wfgiPh5d/mi8TVGcCkIQ0CyMLllD6Z8K2fTlQ0glHC3DYjdDqd/xJdw8y4zk++Wf4W9D4M3bzTwhihZxUsEQQjiEEIuFEKuEEOuEEL9tWn+fEGKPEGJl0zTjOOV3CCHWNO2ztLUvQHE4pb5SXtzwIp+XfA7Ajrod8TVIccrYsobw8ejrCEfD9PvkD5Q80oNNXz6shKOlCAGFk+CaF+C7y82mq42z4Imp8PQF5hBd9T9tFs0ZJRUCpkkpfUIIKzBPCPF+07a/Sin/1IxjnCWlVN5jbcyrm17lwUUPEpMxEu2J3DbkNm4dcmu8zVKcIrrQOefCxwmf+xcWfvor8pY9R79PHmT7V/+i8ayfMGjUd9SAhpaS0hMueAjO+rnZOb7o3/DqjWYiqTF3wMgbzRFYimNyUsGQZpwCX9OitWlSww46GIY0+M+q/9AvpR+PTHmEAm8rZFI7hGjMoLLRz5xNa1leupvdVX7K6mNEogIJVFRmoAmDn1ycyS1jzsBqOXl02WC0kUrfLmr9pdQF9uALluEPVRIKV2HEGtCQOOyZJDhzSXEXkZbQi4yE3tisrRcHKxRppMJXTGO4gYKUITitB/NpSCnj8kDeUbWMmsZigsEgr867Agw/hrabxrEe6sqt9NrRgGPWL6lc/BcSLvkfdm8vQBwSoG///CGfcMQ6TrB/c4/RtE7TO19wwP3DcsfeaYZX/+ox+PCXpif5iBvMUOspPeNtZYejWX4YQggdWAb0Bv4lpVwkhLgAuFsIcROwFPiRlPJYnkYS+FAIIYH/SCmfOM45bgduBygoaN2HXXdASkl5oByAbHf2KR1jS0UZszeuZUtFNbtr/FTUR6lr1AkEXRjRQ0Oeu0DYsNl9WPQYRsz8GRlS46F3K3jo3Q8Y02s9VwzZQjRWh4w2gNEIMT+6DGIjjF3EcGqHv3dYAG/TvCHNH44eAuqhGnPaDESkIChthIWDCJYj3l6OfHCZy8LixWJJxDDCxIwQMlKJS9bj1WJoTUVKgH1aT4TRQDRSS6YlQlhqhKQFw5ZDTsb5jO79bWoCe9lS+gGNwT2Eoz4ikVqiRpCY1LHbUtCFoNG/A02zkZg4nOyk4fTLOhuX7fDkTjEjRmntGqrqN1AfqqY+sIe66k/JogIZmoYWs+EKriRoaMSEgzLnROp6OdhaBP2KV9B761Ys/73klL7rViV9AHxn4cn364hoOgy42JxKV5j5Ppb816x5CB3u2WzmQFcAIFoS6EwIkQS8Bfw/oAKoxLyv7weypZTfOkaZHCllqRAiA/gI+H9Syi9OdJ5Ro0bJpUtb3t3x+9//ntGjR3Puuee2uGxX4KHFD/HChhe4uu/V/Hr8r4+7XzAcZcnu3awqLWHdvjI27mtgT6WdcOiQqriIYLc3kuCKkJIAXpfEbbMwqiCTqT0HMiAjB8sRSYNqgnX8fNa7fLDE9Cz/yahH6ZO8jWDTQzcibBjCAbobTU/Aak3CbkvBaUvH7cjA68giyZVLsisfpz0FQxpU+HZS1rCZqobt1Pl34Q/uIxSuwIjWYJNBbCKKxJSF/T9lcVQFWOIWASxADEEMQRAHMUs6FnsOdnsWrpq30ATUx3QC2JDCRmkYstyZyGgdXqOcBF0SNbO+HkZMmpNFcEB8QoZAILE1/YuChqBGy8RBACMWREdiExFcR4hmoyHwOYZRs2ECmuHkztu/fdxaTmDvAsoX/YzGxm24nD3IzroMuy39wDWb/xB5uB/CYesO3XbkOtmM/YHiubBrAdzbhbzS6/fCX/ofXL6vLn62tAAhxDIp5ai2PEeLPL2llLVCiLnA+Yf2XQghngRmHqdMadNnuRDiLWAMcELBUJwaPxvzM97e+javbn4VieQ3438DQHFVBR9v2cSC7btZui2Cz5fMwfEOTjSLQUZKAwNzJGf37cWo/B70Sc1E01o2iC7ZkchjX7ueXktmIdE4e/wf6ZszFHGC7HwnQhcaWd5eZHl7nVL5QzlZ85JhPEwo2oDTlnTM7Y3hRhYU/4/aqrlYralkpU4g3TsYjyOFREcmTosTQ0apC+wjHIuR7sklEguxvXIhe6qXUVP5Ka7wDpwiik2HUvIIWtzE3H1JcPfB60gn2ZVPXvJQHFY3z21/jmAweEKbndkTKLj0c/bue4MtWx6kOPQ0hdl3UdjjTjStncKAx0KmYHQlvNmmE+CsH0FyYbyt6VCcVDCEEOlApEksnMA5wMNCiGwp5f4BzV8D1h6jrBvQpJQNTfPnAr9rPfMVR/L6xa9z9Xtf5/kF5bz15RPUNyQSi3iatnpxu2sY2a+S/lleBmVnMDqviD6pua3WVq9pGlcOqeK1Nen8+s2lXDumgp6Z2QzI7YvNGr9cBie7Pk3TjysWAG6bm+n97waOH71VF1ZS3PkHlzUrA7LPYUD2OcBPW2hx8xBCkJN9JWmpU9m8+X62b3+UUKiMAf1/3ybnO4YFXcuT2l8Nr3/TrDkB7M9PImXn66dpA5pTw8gGnmnqx9CAV6WUM4UQzwkhhmNWTHcAd4DZBAU8JaWcgZnx862mm9UCvCil/ODoUyhai7yEPH5xxsPc/b86wkBqUh1n9GpgdEEW5/TtT1FyVpvb8Mj1NzP/gedYvCebxW+FgZ1Yta0MzarivIEpfG30JNIT0096HEXzsdnSGDz4UaKrGqmq+rydz96FBKNq20GxAKjdBQ/mQdgHnsym0VS3wdCr42ZiPGnOKKnVwIhjrL/xOPuXAjOa5ouBYadpo6KFXNRvEqunbODpedtpbExmSv5Arj4jH9sx8n23FS/fMZUt+3Zh0y1s2beP1SXVzN/h4MGPbTz8yVcMzaxgxpAUbpoyA7vVfvIDKk6I37+D7Tv+SXX1PFyuopMXaC262lt3/mj48TbTT2PTbLNmkdLTzM3RsA9WPg8liyExz4xb1cJm286OilbbRfnFjAF8Y0Ih33h6Mb96ey1/+Wgzlw3P5apReQzI9p78AKdJQXo+Belm88zkgeY6wzBYtGU5by9by2dbHfz+Ixv/++pVXrhtMkWZhW1uU1dmW/FfKC+fRVbWZRQVtmfSoybB6EpNNu40OOMb5nQkI2+CF6+C/10A7gzoMR4GXGKOsrJ0/RcfJRhdmNwkJ3O+P4Uvt1Tw2tISnlu4g6fnb2dIbiK3Ti7i0uG57WqPpmmM7zeK8f1GYRgGby76iF+85+Gify6hZ/In/PDcAZw1eEK72tRVEAhstnQGDvhT+/qOdBWRaC4FY83ghhtnmZFwi+fC+nfAkWTmNZ/6czOWVRele9WnuiG6JpjaL4N/XT+SRb84h3svHkggEuN7L6/kmQU74maXpmlcOf48Xr61F6Pz6iitd3Lbi+Ws3L4mbjZ1ZhITRxAOV9DYuCXepnR97Akw7Bq4/An44Qa48W3oMx2W/hf+1BvmPgT7uubvWAlGNyLFbeObE4t4/3uTOWdAJr+buZ4tZQ0nL9iGjOw5lP+741b+c+MgooaVVTt3xNWezkpy8ngAGhrWxdmSboamQ6+z4Iqn4Ox7IbWP6S3+70lmaPVNXWuMjxKMbohV13j4iiFIKfn97A0EI/EPvJbhNZ39iitq42tIJ6W2bhkAsZjKBR43Jv8Q/t9SuO5V6HU2VG6Fl74Or99ippLtAijB6Kakeuz88sKBzN1UwfVPLaK6MRxXewrS8xmVs4+XVnr43rNP89qCrvVm1lZEoz42b3mATZt+TXLSODIzL2xfA7SmmGGzf2wOQVVA3/PgxjfNyLjTfgVrX4e/DoLHJ4FhxNu600IJRjfmlklF/Ou6kazZU8fXHpvPntr4ZiR75Otnc0ZOFbM2pPKTdyP4g+pt+XhIKSkvn8PCReexe/f/yM25lmHDnsZqTWpfQ0bcBH3PhyVPwt9HqBwTh6JbYcqP4a5F5nLZGvhzX6jYFF+7TgMlGN2cC4dm89Jt46jyhfneSyuIGfFzwirKLOSl79zKj6fFkGhsK9sRN1s6MoYRZfWaO1mz9i6s1iRGnfEa/fs/gK7HYVjn2tfN0UJCg4GXqlAaxyKjP/xkuznfWAGv3Qz1pZ3SQ14Nq1VwRo9kfnvJIH702iqeX7iTb0wojKs9Zw0ayp8/W80PXvqEt75XQILTc/JC3YjSXYv56r8zMCJXkT8gFbnXi27ZTnKWm4QUh7nTkRHMOTo8ihGTZBQmnN4w3GgIjChYXWbUWt129Pba3VCzA2q2w57lkJAFZ/4UrI5TP29nw5kM038HH/0GytfDXwaY678xE4omx9e2FqAEQwHA5SNzeXNFCX/5aDM3je8R18Q8fXN6880h9/OfVVdz5sNv8fjUEHlajKrSTwhESrCLdFIsZ5B3xU/RupCXeKS+kvVz3yNs60l9dQ1W59HXtm3dMj5+qpxYyIsnRaOxNsLuDbuQp1gzPPfWQfQZlXnqRk/+IfSbAZ/eD589AIufgF7ToK5JJOpLOWbokD7nmk5v3QUhYOL3YMJ3Ye8qmH0PlCyBZy6Ckd+A3mdDxkBI6xNvS09Ii8KbtxcqvHl8+Nkbq/l4QxlLfzU93qYQiwX4bO1Cbn3Jz9kFc7mu/5sQA0udlWhiBHSw7hCMuuhDXBlHJ7qp3vopleveBSlIKZhG8qCz0e2uOFzJQQwjRsOO5dTvWoDVmoLFnULdngU01K6lOnktMYuNzW/+C4Da5DVIESWHdLL7QmpOCrXlPrbMywDggu8m0HPgaACikRjrvigl2Bghs+gQL/6mCOSBhjDFKyrYubbqMHv6jc3irBv7o7dWyJjdi03hqNxiNk0da6ovhecuM2skd31lvnl3VxrK4I1bYMc8DojqZY/D8OtO6XAdLry5omuzpdxHQUp8H6r70XUn04acCS+9zye7pnL/0EEkDzgLZ2oPAru3sPXp71AxdhuL511AD+1a8s79Hha7F3/xOkpWPkFJ0vvQlK9od+27MB8waHacPBEFvQYsQTcOSwY2ayZOVx6u5D64MwfgzhuCxd28ECtbZ/2CiuqPCCTXIF1NBoSaJqfZ/C/toBHGYSsjGDbf+K3OEIE9eRQvhGIADn436dmFB+YtVp1hZx+MkgtgGJLlc3aydWk51aU+pISkTBe9R2XQb0wWSZlt8D3nj4FvvHfifRKy4Opn4dlL4cNfw6X/bH07OgsJmXDzTNjyMbz+LQjVwby/nrJgtAdKMBQHSHbZWLazmoqGEOkJ8W/q0TSNPv5ytrgyyJl084H1zvw+DLn3Q/bO+x+bG/5IcdJzFC94DhExH7ykgq3CwdAR/0VYdSo3z8RfvQ1DRJt9bsMIEgyWEnUHqXfuxPA0dVpGgT3AbtB8Gla/A2ssAZuWisORjdNThCutD86MPsQijVRvnsNO5yuQC0l7e+ONDiYhewTRSAPRUC0e70BSpl6EpukY0RhzZr5McdMIZ6du485/TqGxvhZfXRWN9fXUVlZgs1txJ6ae0P592+pY9I4pM+4kO+ffPpjMIm/HyAE+72/m54rn4KxfmvknuiPRMGx+H169CSxOGH4D9Ds/3ladECUYigP8+Lx+XPzPefzk9VU8ffPoDvFwSY0FqI40HnNb9qRvkh66ij1fPI6vZg0Rw4cjIR9v3mgyJ1+BbnUC4C0afdp2RMON+EvX0li2AX/1VgK+XYQi+whRQ8hRQ6O7DOlYb+5c1zQBNPUBZ5SPYcj1L53wHJWrtlHsNx+eLj2EMz0Z3WLBm5KGN6VlaUJdXhvuJDuNtSEaa0N8+NQ6xl3Wk4JBqTjc1hYdq9W58M8w8wew/YuDme0KJsDNszp/9FcjBuFGiATAX9nU2d80hXxmWBGH18wpvvIlKG/yzD/3fjNsegdHCYbiAP2yEvjFBf257731PLdwJzeNL4yrPb6aOhYm9ADgyzlfMfm8oztJLXYPPab/uM1tsdjceAvH4i0ce8ztUkrCNaX49qyisWIzoboSNIsNb9ZoUkdchGY5+UPak59OQmQFDdZ0/DE7stzHhq/2Ig2JlBIZkxiGeS4ppbneAGlIwqEYNXsbGXtpT1JzPCRlurj5oYlIQ7LwnW0sn7OLj542Be07/57Wqv+bFpPaC77xrpl74h8jzXX71rS9WEgJsTBE/BD2mw/1SNPDff9DPuI/ZPv+qbnb/ebxj4UtwRSJUAOE6jmsbXT83Z1CLEAJhuIIvjGhkNlr9vHvudviLhjOBA+/zg/yn61hfvp+LfOmj21x2tj2QgiBPSUXe0ouqWY6mBbjykjmxqeu5sN7XmAp4G80+PSZDS06RrAxwuX3nAFALGbw4ZPrKF5ZcUr2tDmbZpuf6f3hlo+aV8aIgb8KfGVNU/khn+VND+79IuA/+gEvWxgGR2hgdYPVCTaX2VlvdZnLzhzz87Dth8w7k5s6+4vM+f01dsMwEzKF6s1aR+rppyBuL5RgKA5DCMGIgiQW76impMZPXnL8OsF1i84t37mCxd/9F3Nchbz/+qdcePU5cbOnPfDvrWZfnQ3SwZVgZdT4QoQQaFqTH4Um0DSBECA0gdb0WbG7gfXz9tJYF2bJLLO/JRyIUryyguQsF5quEfSF6T+hg/QX1O2Bz/4ARWea0V6NqJmgKFALwbpDplqzOWfvKtND2l95MG3qoVjdZlhxe4L5QLd5zHwVNtcJHupNn/tF4EhBsLlNv5LWbprVtKZmqbbPS9PaKMFQHMXVo/P5zxfF/OOTrTx85dB4m8NV5wxlzoJ67l4W4Kv1z3Dvz6/Fao9ffvC25KMHZtGoZZGSWIol1cXYi48eMnwsileWs37eXuorAix+b/uB9ZommHR1HwoGnriTvN3Z/IFZE9izDP7cDxrLj7+vboeswWaMpoRs8GQ0TZnmpzsD7Mq5sz1QgqE4il7pHoblJfLRhjIeNCS6Ft/O73MumcyqiXX85s9v8nwwg37Pvs+Nt10aV5vaAsMwKA8nk++uoDY3lWAw2OyyKdnmA3PMxUWMuqDwsG0izt/fMckaAtnDwJkCSfngzQV3utnO70gCZ5I5b/eCK8WMy6SIO0owFMfk2jEF/OzNNTz8wUZ+OL0vDqseV3sSUxP56wPf4PN7XuejTQ0cM6F8J2fvvLVELG5K/RouqltUdu4LGwGorwycVCCklERCMSLBGOFglHDTZyxiYHNYsLss2JwWrHbdbI0RAgFY7Dpaa4lP/hi444vWOZai3VCCoTgm0/pnML5nKk98Ucy+uiB/v3ZEvE1C0zTOcfl5I5JBQ00dCcmJ8Tap1ahYsZmlr64GcojqToLVDeBq/lt1OGh25hYMSmX3xmp0XUMI+Ojp9UQjMax2HSMmCQeihEOxZjswHonNacHhtjDs7AIGTc5pPS9xRadACYbimGR4Hbx0+zh+9OoqZq0pZV9dkKzE+AeLO3NwLq+vDPP+O/O4+uZ2zv3Qhqx/dzUl0RysER+9kyrZ7XURjkaaXd7hNm/lD586OuNeYoaTzEIvukXD6tCxOSzYHBZz3rl/WUezaESCMUL+KOFAlEgoxv7QQVJCJBgl6Iuw5vM9fPnKZha+vY3MIi/OBBuuBBvedCcOtwW724rDZcXutpCQ7EC3KlHpKijBUJyQa8bk88byElburuX8xKx4m8M5l0wmbdHb/Hy9i4vr6nEmdr6RJseiYGwRa9+uI8vdwLQ/f4vnnnsOWiAY598+hNpyP0ZMYsQMYlFJLGrgrw/T+4wMbI7Wu9XHX96bko3V7FxXTcXOeuqrgvjrQkTDR49eciZY6TEolSFn5ZHRo2t8V90ZJRiKE7K/xdreQd4SnS4Hdw1O4Hdb4NLfvMnjd0ym18DOM479eKx4Zz2Qy+5wNlVrtrW4vM1pabcHstWuUzQsnaJh6QfWGYYk1Bgh2Bgh5I8SbIwQaIiwY3Ul21dXsmlxGfn9k0nOcmN3m/0k3jQn+QNT0PWO8dtSnBwlGIoTMmvNXjQBvdM7zrDFb35rBt7X5nLfIg/XPrmYJ69uZNj4+A//PR1mPHAx7//iLUpFD2b/bQlMjbdFLUPTBM4EG86Ew4c7D5iQTdAXYensHZRsqmbvtjoioYPOcwmpDgZMyCYciBL0R7E7LXiS7dic5qPJlWDDlWjDnWjHmWBFU+ISV5RgKI6LlJL/zd/BOQMyyO8gUWzBdGC78uqzKMxazk3vbufSd3bzwLod3HDrJfE27ZRxpCZx2b9v5j+3v0+9NQO5cyX2jI4j0qeDw2Nl0tUH8zzEYgZhf5Rd66uZ+/xGFr+3Hd2q4XBbCfkjx2zaAkBg9pd4bbgTzVhZaXkJZPRIIC3Pg8UW35F83QElGN2YQDhGTEp0IdCbvIf31QXZXe1nZ7Wf2Wv2AjCioGPmLBg1ZSSP7KvmO8tDLNhayXWG0WFDhzQHIQRnDIqyeCPUNep4a3zxNqlN0HUNZ4KNfmOz6DMqA2mAZhEIIZBSmp3uwShCCPx1YRrrQvjrQjTWhfHXhfDXh/HXhynf2cCG+eZvVLdq5PdPpsfgVHqPysTusnSI4JldDSUY3Yh9dUHeWrGHzzaVs63cR1XjcQKlNZGeYOeXMwZwy6SidrKw5Zx57lj6LHiT2Y5MrrzvFZ758cUkJHTON/ParaWsXR1G10N47SEcKR1TqFsTTdfgkIqBEAKH23ogou6BlLPHQEqJryZExa4Gdq6tYs+mGnasqeLzlzYDHOgnye2bRP6AFLL7JGFVtZDTQglGN2BnVSO/fW89czeVY0gYkpvI9IGZ5Ke4sOqCmAHRmEFMSrK8DgpSXBSkushOdMbdy/tYGIbBkrJ65hRXsnR3LXsLC2Cfn+VhLyPfX8rZkRoeOG8KaWkdLBzGCZBS8sEf5xLSk7jgynS+KM9ukaf3kRiGwca9DYQCUWxSUFiQgNvetbylhRAkpDhISHHQc3g6UkrKdzRQsqmaaMQg5ItQva+R1XNLWPnxbnSLRnbvRHoMNkdtqc72lqMEoxvw/MKdfLrRjNUzsXcqM4ZkMzgnkX5ZCXH34D6SikCYNeUN7GkI4g/H2F7jZ0ulj9pAlEjMoKYuRENNECIH27kdiTZ69UlmaCpU+8p4O7eIt9fsZvskL05r+z8kG+oa2LxzF0P69cHWzJhXOz9YSpWWxcjCWnqcNwqea1mUWjBFZ0tJPTO3VvBuVR0bUw/5bkvMj58bHr53du8WH7szIIQgs8h7eJpaIBKOsXdLLbs2VLNrbRXzX99KRqGXnN5J8TG0E6MEoxvwnbN6Y7foLN9Vw5qSOuZvNXM7WzTBGT2SOX9wFl8bkUuS69QC+r193bfxbllLcVIu/xl7JQ0JSVgsGlabjtWiYdEE4ZgkFI1hGBIdcDitWC0aujiYObW6Ooi/OoA4os9TChAWDaEL7E4LRUVJDMzxMrUwhfMK0/Ee4mMQk5LcuasAuOqND3n+gskktbGvRswwWLFuI59s3MaXYcmq9GwiViuD3vyQ/w7tReGgASc9xvL3NmGJJjPyjubnUy+tCfDksl18FQzSIA3qkVR4zLdml1twRYNO/6wEQjHJo75aIhbBHzQfl9f4yY9jFOL2xmrTKRiUSsGgVPadkcEbDy8jHGh+9kXFQZRgdAOSXDbuOa8fYL6F7q4OsK60jlUldXy2sZzfvreev360medvHcvQvKRjHmPt7M8oX7ISicTA7JyUmB7A2euWYGgWJu5excTdq7j5m3+hETuBxgi+qIFhSDRNYLFoaJogIiX+ujCGIc0DNGFzWRkxOIOBOV4yEhx4rDo9k52MykzEa2veT1UXgn1nDef/3pjJL9KymTx3OTeEG7hu/Cjy81ovtHdpZRUfLVvD3Ko6FiYkU+PxQlo+vSv2cV11KRlWC/9MTmf69ip+vPw1br36EjT7sdPe7l1TzApvPj1t1UTcTmxNyZFiUcmW3XWU+cOUByKUBcNUBCNsawyxhgilLgE6ZGCQFIMCXedizcnFPdMZk5uIfsgAgG+Howz5dDV+u8ZLa/bykymd33flVNjfh3HckViKE6IEo5shhKAg1eyjuGBINt+e2ovHPtvKf74oZtbqvccVjKr77iW7/viJeLacfTmaPYx39kz+88qvGbpkEZoev+aum6+4iN5LVvBARQN/zcrn0U17mTF3Id87YxCD+vVu8WgqfyTKvNXr+XR7CfOwsDU1A2xJJCVojK+t4EwR4pwRg8k7a/iBMhftLef7C1bym7w+vP3KbB4Z1odBwwYfdezfLNvKe+dlAIn8fP5arFHJeTU+rLEI923dftT+NpukZ6PkzEYbVw7MYmLPk/fVuG0W/Hbzmj+squP7MQNbN2zDtxwQjBYmUlIASjC6JbHaWuo/+IBFb37I7kof+1J7kps/lGvHFBy3jGYYrOk7hkmP/wlNmENwNQEaAk0I+qUnoWkac4RGwax3mXPv/VzwwH3td1HHYNLoEXwwegSbtmzjqSVreDkzj5n7/PRc+xH/GlDAiMEnbioKRqPM/WoJT5ZWsyQ5nbDVhtWbztCyPXx/bzFn9yli5KTx6MfpJ+mXncGsy6fz9GcLeCgtk/PL/Nzx4pvcc/mFOBxmbaO2rp6Ps7yM2lfOWdm92BsM4xOSZDQsaPzA5iXVbiHTYSPTZSPLYyc31XlKHbZv9Crgim27WJukUdUQIjvJ2eJjdHasdiUYp4MSjG5G3Xvvse+3v8Pw+chMTsMWMphUugbWvMOuLx5jV9N+R46NSm6sodphIyc3/chDHsb0R/7AqzHJsNdf4dOcHKbddXubXEdL6NenF4/06cV3S/Yya9kq/mFzctXOauan7iMz+2B8rFA0yvx1m5i/vYRFoRirk9MIW52kODxcXrKdaVkpTB09Am/66GafWxOCW6dNZEZlFT/9aAH/zO7J7Hc/45EeaUwYcwb3zfmSQHouv+plZ9yYg81Ez5UvIxgMctvE5iVQag6vry+DplaxSz7fQA46abrGuTnJXDU8p1P7sDQXi828xkhINUmdCicVDCGEA/gC86dmAV6XUt4rhLgPuA3Y307xCynl7GOUPx94FHO09VNSyodayXZFC6l+9jnKHnwQ16hRZP7yF9j790cIwdwnXqb+40+OSkUpD1msBnKu/fpJz6FpGpf88fd8vmo5hX//Kx8lJzP92qta+UpOjfy8bO7My6Zo0Qq+4Rec99U6zgouxI/Aj2Cdw01pcioiIZ0+/lKuLN3BhIxkzj93Mh7n6UXqzUlL5blrL+adzxfwa5uDK30aGW98TFl6LgBjRw9vhSs8MRcXpvHBtt0kxMCuCbbpMZZbDWbVVfLQrHIuc3m4fWQ+2cntU/MIB6MEGiJ4Ux3tluRpf5NURNUwTonm1DBCwDQppU8IYQXmCSHeb9r2Vynln45XUAihA/8CpmMO7FsihHhXSrn+dA1XtJza117FMWwoBU//F2E7OCJq6u3XwO3XtNp5XDYr/Z7+L6ELzsfzlz9TNnXyYW/y8aYgyYPeUMe+5FTe8ztwRsI4IxHSgn5+WB/m/BGDSTt7ZJuc+9IzJ3BWg49fvz6LVwrNgQgv2sLt4pU8rX86G/ofXkMMhmP8c9EOXhH1PK75eXzlJoZVGXy33ELBwBT6jsnE3oy8HMHGCJ89t5HqvY2k5rhpqA4S8keJRQ00XWBtCqFuc5qh1evK/ZTvbADAnWQnp08S4y7tiTetbcVK0wS6RVNNUqfISQVDmgHx98cosDZNzU2/MgbYKqUsBhBCvAxcCijBiAN6WhrBtesIbtmCc9CgNj1Xz6IerH7mORy33sLy2+9kyisv4XZ1jDbzAf36UNJXxi10hDfBw6Pf/DrXrdnA+j37mHbWWXGxwzAMlpaUsCiwl0YMwBx+XOnWqNzjY8fqSr542fSavvJno0gvSDiQca+yxMfH/1tHOBgjKcNJXUWA+krT0bC2zE9uv2QSM1zoFmEmbgrGCAeiNNaGqAn68deF0K0aI6YXsHdbLVuWlLFjTSVf/+UYEtPb9neiWzWiEdUkdSo0qw+jqaawDOgN/EtKuUgIcQFwtxDiJmAp8CMpZc0RRXOB3YcslwBjj3OO24HbAQoKjt/5qjh1su+/n103fYNd37qFnm+/hTW79YaZHouhY0cx99f3UvibX/Lhd3/IJU/867ChnvGkI8QZGjtkAGOHnNxHo7XZV9/AvcvW8UVIUmN3Iixusn2V5IbLuTzFw4/PG4f1fEHp5lrmv7GVil0NvP7QUnSLRlq+h7Q8D+u+LAWg14h06quCh0WgvfnhibgTjz2E+His/WIP81/fwtt/Wc6Yi3tSOCT1qMi3rUEsZhCLGqeccbC70yzBkFLGgOFCiCTgLSHEYOBx4H7Mf/39wJ+Bbx1R9Fh35TG/KinlE8ATAKNGjVJfZxtgy8sj+8Hfs+vmb+L78kuSr766zc859erLeW/7Dvr/70neffARvvarn7b5ORXHpjYQ5OtzF7PKngDCQb70cb3D4Ir+vVj/aTFr1qxhyMiRWIUZCDC3XzJX/2I0dRUBdq6tpKEqyK711az7spS8/slMvLIPaXmtE7dr8JRc0vMT+PC/a/n02Q0gIDXHTWahF0+K42CGQIcFu9OC3W0hOdvd4thQGxfsJRYxKBiU0ip2dzdaNEpKSlkrhJgLnH9o34UQ4klg5jGKlAD5hyznAaWnYKeiFZCRCBWP/h3hdOIa3fyRPqfLhT/+Pu/t2E7/5/+PD4uKOPf6theqzkiFr5FAOExuK3umLy3Zy73L17PWmUDI4aV3oJ57euVyad9hB2pagaIi1qxZw/Lly9m8eTNDhw5l5MiRpKWl4UzUEWm1uJKiXHbhUHTNSoOvjvLy3exdE8UwDKSUGIaB0RQx2OFwEI1G8Xq9FBYWNsvOzCIvN/xuPBW7zWCCe7fVUbyqkqDv+JkH7S4L2b0SScn1oOmCpAwXTo8Vu8tKap4byyGhb6SUrPpkNxk9EugxuPPEGetINGeUVDoQaRILJ3AO8LAQIltKubdpt68Ba49RfAnQRwhRBOwBrgGuax3TFS0lsHo1gRUryPjxj7EXtV8EWk3TOP+vj/DJNTeQ+4cHWNojn1GTxrfb+Tsy83eX8s8NxSyVFhps5kgsayzKRZW1uGMR7pq7iLEpXm4Y1Be9hY6QX2zfxR8372KpzQMJqXjDAf6Rk8Al/YYfte/IkSPp2bMnxcXFbN68ma+++ooFCxbg8XhobGw8kNv7nXfeORCGvLn86le/wmJp3rup0AQZPbyHZQ80DEkkGCUUiJo5xwNRAvVhasr8NFQG2LOllp3rqpHG4TZpFkFiugtvmgNPkh3dolGzz8/YS3t2iCbJzkhzvsVs4JmmfgwNeFVKOVMI8ZwQYjhmE9MO4A4AIUQO5vDZGVLKqBDibmAO5rDap6WUR2epV7QL1uxshMNB/Zw5pNx0I6IdA/PZHQ7GPvE4a6+4CtsPfsD2l1+iqFfHDZve2hiGwac7Snhj206+NCz0JcpeQ7Dd4UHXnAyK+BmqCewWnd2BEFYkISl4y7DyZlWIn36xhhu1EL8ZOwy3zXZMn4lIzGB9RSXPbNrOi4bZh2DRHVwQaeC6PoVML8o/qsyhJCUlMXLkSEaOHElDQwMrV66kqqqKhIQEevTogaZp7N27l2AwSFJSEllZWdiabNE0DSEEmqZhGAbBYJA5c+awfft2du7cSa9epx6KRNMEdpf1pKO1YhGDuooAoUAUf32Isu311Ozz46sJUra9/oQ1FUXzaM4oqdXAiGOsv/E4+5cCMw5Zng0c5Z+haH+sOTmkf+97lD/8MP4VK3CPGdOu50/NSCf38ceovfFGtt1xJ8mvv0pSUmK72tDeSCl5fvlq/rSvjjKXFywJAFQCyUE/14kgPxg1gPwj/g/P7S0mGAzy4PiB3LdsLS9ELDxn2Hnuq43osRj2WAR7LIrdMJCaRkwI6qx2IrqF/d55I0I+nhg7mPzkpBbbnZCQwOTJk49a37Nn8xwJ6+rqKC8vx2q14vW2T65x3aqRkuM+sNxrRMZh26Nhs3bi8rZ+Z3p3QXl6dzP8ixaheTzYe8cnxHXvwQNZ9NAfyfrBd/nizu9wwbP/w2rrWnkaAJaXlPLulu38m6YHmMvLZdEGfjFmGA5Nw6rreO22kzYzJTgd/HnSKO6qqeOFDVupiURpQOITEp+mE0BHGAYWaTDECDLY5WBkeioT83PwOlo2Uqm1KC4u5tlnnwXgjjvuID39xNEB2guLTVdpXE8TJRjdjFhNDXpyMnpy/LK5jT3vbOZ894f0+dufeO+X93L5Iw/GzZZjUdHo57kN2yhp9OPQBNOz0zmzZ49mhc4wDINbP1vIbM0FTWJhQbJsbH8yT8MPpVdyIr+ZcMYpl29PDhVBj6dzZj9UHBslGN0Ma0EBgVWrMOrr0RPj1xx03p238Pb2Yga88yYzi4q46K7b4mbLfmKxGJ/t3MMNO6ub1tjBgKd31+HYtoS8oI8+CS5y7DbSbBYsQlAWjrCuIUAwGsWua6zV7PisZq6JV4tSmViQ22F8T9qLHj16MGPGDGbPns2OHTsYMmRIvE1StBJKMLoRjQsWUP/eeyRdfXVcxWI/F/3+t7y/ezdF//wb8wp7MGnGuW1+znA0ytsbt7K4qo6tgRBZVp3ycIwdaJTZnMQ08+34DkuYH5wxmMpgmNc3b+fzBh8lNgcfGVaiYR3CEnO8h44bC3aLThRBsKl8WrCRKYXD2/x6OiIrV65k9uzZuFwuesep6VPRNijB6Eb45s0HIcj8WcdwnrNYLEx9/J8suvJqkn7xMzbk5zJgSNuFLPnrsjU8XL/fI9mOzaIT1iy49RDpRpRzZZBedgdX9y2ib5rp2JXkcvGzcSP4WVOpWCzGnrp6Sht8CE0n1WGnKCXpsGaYffUNRLux62k0amazk1ISi6mYTV0JJRjdCOfQISAllY8/Ttqdd6K53Scv1MYkJHrp98R/KPn61ym/6y5SX3uNjKyMkxdsAVJKfrZgOc+EzYf6kIif/xs3hJwEDxHDwNYC/wZd1ylISaYg5fh9QFnehNO2ubPi9/vZvt1M+hQIBGhoaFD9GF2I7tW42s1JOOccEi+9lKonn2LL5Cns+clPqHnlVYKbNiPj+CaYX9QDx98eJbG2hqV3fJtAINhqx24Ihbnik4U8E9bxhvx8PDCXj86dQK43ASFEi8RCcXK2bNnCunXrsFgsXHHFFWRldZwoxYrTRwlGN0JYLOQ8/BA9XnqRhPPOo3HefPbdey/bL72UzaPHsPvOb1P7xpvE6uvb3bYRE8ZS9cvfULRpPR98/x4M4/SjiW6tqmbKp4tZoDu5OFzPkikjGJzZMYZ4dlXWrTP9cs877zyGDBmiPKq7GKpJqhviGjEC14gRSCkJ79hBcPVqAqtW4Zv7Ob65cyl7+GHS7/4Oydde2y7e4MHNpdS+t4IBDQbGZU8wGlj80HuM+8Wlp3Xce5evZ6/Dw4MpNr41bErrGKs4IcOGDWPz5s3MmjWLWCzGuHHj4m2SohVRgtGNEUJgLyrCXlRE4qWXIn8tCa5ZQ8XfHqXswT9QN3MWBU/8Bz0pqVXPG6muo+GTFYS3VRLeJ9AcGYAXGXMgmlqI8upT+HLeTiZP6nFK53ht7SY+sZht5zcM7tdKlitOhN/vZ8OGDQeWU1JURNiuhhIMxQGEEDiHDiX/v0/R8MEHlP7kp5Tc8wCJl1x++I6HjgBqyuMqj1x/3P3BtziMGZosEyhDTyrFM74XnimTEEIQCEZZ/I+l5M7exdp0F4P7tbwZ6be7KsDpITXkV/0U7cTGjRtZu3YtFouFH/7wh7hcrnibpGhllGAojkIIgfeCC4jsaySwKRPfgtbtEBdCQ4aqyPj+ROw9jo5X5HRYGHDLEEr+sRL9pU2UfsdJTnrzR9psr6mlgBiVQI3Vjj8cxmVT8YPamoEDB7J69Wp27NjBH//4R8aNG8egQYPIzz9x0ENF50EJhuK4pNx0BaW/nY8MSxx9dKw5+hEpsY6oUhyvf1Mc8iEl1sIcnP2OFopDyUhxU3Njf8R/11P83zV4v3cGHufJH/orSsu4Zs126pyJ9A35uC47VYlFO+FwOLjpppt4/vnnKS4uZuHChSxcuJC77rqLjIzWHSqtiA9KMBTHReiCzB+MouqFDQS3+LBkZpF4fiHC0j6D6/r1TGXBJYUUvLWD+f9bzTl3jETXj3/uF9du5IcVQYTVxl/THFw7ZHi72Kk4iKZp3HTTTQCUl5fz2GOP8cILL3DzzTfjcrmw2Wxq5FQnRgmG4oRYkh1k3DmMutnb8c3bg39VBZ6JOXjGZKGdJD9BazBhbD4fljUyaEEFM19aTd7UDGbt3seeYJhQLIZHE3itFirr6nnXnQZS8kyPVM7tdWqd5YrWIyMjg8GDB7N27VoeffRRANLT0/nOd74TZ8sUp4oSDMVJERaNpEt64RiYSsPnu6n/YAf1H+7AXpiIo38K9t5J2HLazpv3nIv6Mn9TNWesbeB/jSEe62tHM2xoSKJChyjgTqMo6OPJM/ozOCOtzWxRtIzLL7+cUaNGsXnzZhYsWEBFRQVSSlXL6KQowVA0G0fvJBy9kwjvbSSwqoLAhirqZpthIGz5CSRMzccxMKXVHwaaptHvhr7s++cqvrkdBuUYXDB9BDarlaiUVAeCNITC9E5JalYI8kOJRCP4Qj4agg34gj4aw400hhrZU72HouwiJhRNaNVr6W5omkZhYSEZGRksWLCAlJTW/30o2g8lGIoWY8t2Y8t2k3h+IbH6EIF1VTTM20PVc+vRk+zY8hOw5niw5Xmw5XpapekqIzsN1w9Gs/OvC+i70ODifedTYak5sF0c0eO+f/l46yWSmIhhiBN4lG+BG7bdwJ0T7yTRGf/ovp0Zn88HQGNjIxs3bqRv374tFndF/FGCoTgtdK8dz/gc3GOy8a8sJ7ixmnCpj8CaygP7WAYlsndQI1pUEAtGiIYiRIMRjHAUIxTDCEchLBFhiYhItIhAj2pYohq2mAVbzII9ZsMmrSQ2JSW6vewKvhpUDJjBBSUSKc1RW/vnD1t3yLLEbBJx6A6cuhOHxYHD4sBldeG0OnFZXXy570vm18/n+T3P88rLrzDBO4HvTvwufbP6tvN/uGuQnp7OhRdeyBdffMHLL79MQkICubm5eDwehBAYhkFNTQ21tbXk5eVx2WWXKUHpgCjBULQKQhe4z8jEfUYmAIY/QrjUR2hLLXWf7yZ93aFv+tamySQoQgS1MCE9TFiPErHECFvCxOwGhhUMK2DV0GwamtWCsGoMGziVqwf+vza7nuu5nlgsxuxVs3l27bN80fAFC99fyEVpF3HbxNvITclts3N3RYQQjB49mpEjR7Jp0ybWrl1LWVkZu3btOtCnkdyUBXL16tX4/X4mTJhAjx49TprGVtF+KMFQtAmay4qjdzKO3snsKqjm7RVvEBBBymIVlIT2cF6fC7h26PW4XG6ybG4sWsf7Keq6zsUjL+bikRezfPtyfv75z3mj+g3eevctBtsHc93g6zh/0PnomnqgNRdd1xk4cCADBw485nafz8esWbPYsmULW7duxW63M3nyZCZMmKBqHB2AjneXKrocQwadwZBBZj5qKSU3zL6BN/e9w7CikZydcnacrWseI4tGMqdoDou2LeK/i//LssAyfrb8Zzyw5AFyLDn09PZkatFUZgydoTp1TwOPx8PXv/51wuEw27ZtY8WKFXz88cesW7eO3r17k5ubS+/evbFY1KMrHqj/uqJdEULw4OQH+fHnP+b7c7/PZb0v43cTftdpHrJje41lbK+x1DTU8MLiF/i09FPKI+Vsqd3CBys/4PnVz/PgeQ9SlFEUb1M7NTabjQEDBtC/f39Wr17NggULmDdvHlJKHA4HAwYMYPjw4fToofxt2hMlGIp2p4e3By9c+AL3LbiPt7e+TWOkkb9M/Uu8zWoRyQnJ3H323dzN3QBU1lfy0KcPMaduDpfMvoR88unp6cnfL/+7ako5DYQQDBs2jGHDhhGJRNi5cyfPP/88K1asYMWKFdxzzz0qo187on7JirhgERYWli4E4KOdHxGKheJs0emR5k3jT5f9iUdHP8pgy2B2s5vPGz/nG69840COa8XpYbFYKC0tBSAzM5Orr75aiUU7owRDETeuH3j9gfnpr03nvW3vxdGa1mHawGm8dMNLrLxxJed6zmVleCW/n/37eJvV6QmHw7z55pt8+umnDB48mNtvv/24HeeKtkMJhiIuCCH41uBvseqmVTwx/Ql0TeeBhQ/E26xWQ9d1/nT5n0iIJbCybmW8zenUbN68mccee4w1a9Ywbdo0Lr/8cjXUNk4owVDEFU1ojM8Zz4yiGfijfv6y7C9srtkcb7Nahd1Vu2nQG7CorsJTor6+nldffZUXX3wRi8XCzTffzJQpU1SfUBxRv2RFh+C2IbexrXYbz6x7hv+t/R/D0ofx2DmP4bV5421ai4nFYjzywSO8Xv46mtCYnHPi3B/dkVAwyLJ5n5ORnUvvQYMP22YYBkuWLOGTTz4hFotx1llnMXHiRDWUtgOgvgFFhyDJkcStQ26lxFfCzvqdrKpYxV7fXrwpnUswguEgFz9/Mfv0faTLdB6a8hBjeo2Jt1kdhmDAzxvPPM22kr0YFtPb3/ryy+RlpjNm4iQSM7OZOXMmpaWl9OzZkwsvvJDU1NQ4W63YjxIMRYfhra1vsbN+JwBFiUX0SuoVZ4sOJxwJYRgxHPbj56q+b/Z97NP3kWQk8dHNH7WbF/iffnsvPikQRgyk5LD0h+KwjxbRnDICEEdkXzSDPEpcdhs333kXVpuN+R9+wJKVq4noFhLsNkYMH05NVSXbduxke0U1298xBz3YLBauuOIKBg8e3Gn8c7oLSjAUHYb7xt/HqMxRPL32abbXbWfqq1OZkD2ByXmTmZQ7iWRHcpvbIKXEF2ygvLKEqup97Ni7md0b1uDbvgdHRQQEiJ5pDB99FtMvvglxRHt6QVIB1MHkpMntGjLE4/Hga2hEajrpDhsWXTMf4Qf/HDqLPPQBLw/OyyOXj9p2yEEO2W4cKHbgBBhAbTjG3/7+D3OjpqEB08aNYcr5Mw6zv2JvKV988D7bNqxFKynmwzUL6fXE87gSk07hv6FoK5RgKDoMVt3K1/p8jUt6XcLnJZ/zya5PmLdnHu/veB+nxclvxv+Gi3pe1KJjSinZU7ad6sq9ZOYUktkUNDBqRNlbtZsN6xezZs18fDtK0asCiIjEFhVo8uCbrSEkIlVHG55LsKwa15Yq1mx5nUXvvo5h18xXbItO2vjh/Dv0OgAho339Sm7/wY948s9/ZG9jEIvFwh33/KRdz388vvhgFl99tZDkxASGnzGKERMnYbUenWM9PTuHK755C8FGH//61jUAPHn3LfQYOpwBk6bSd9wkVdvoACjBUHQ4dE1nWsE0phVMw5AGG6o28Mclf+TnX/6c5WXL+emYn2LX7UeV21m6hc8+e5WafXsJVNUQ9vnQ6yO4AgdrAUG7gQT0GNiiB9friQJyk3C4PTg8HlwpKbgTk8hNLWTY4El4Eg7mw9i6eSXvv/A4AX8NmmGY0VbrggTfWsKU7DQqUg2m9BrOB2++htPpwuly407w4PZ4cCd4Sc3MarWRPtFolMVzP2XNihXs9QVACKz2ox/I8WLK+Rcy5fwLm72/w+3hR6/MpKpkNys/nEnx8iVsW7qItIJX6Dt2ImdceCk25/GbBBVtizi0ytlRGDVqlFy6dGmLy/3+979n9OjRnHvuuW1glSKeRI0o/1jxD55e+zQDUgbw4KQH6Z3c+7B9HrzvJuwbqolpkqALcNuwJ3tJLMgjITWdmn178JVXIISG3eHEnZRCVs8+jB85nbTk7NOyLxBs5G+Pfg/b8n0A+Hv0I+ZKOOa+NiNKflYWLpeL5JQUGurrSEjw4vR42Fm8DTSNvTV1uL2J3H777YeVlVJSXbaPrevXsWH9OnaXVRDTLSAl6U47X7vuenIKuk58JWkYrP5kDhvmzWXPxnVk9uzDhKuvo2jYGUc1B3Z3hBDLpJSj2vQcSjAUnYnPd3/Or+f/mr6rNAYUe/ClH+wnsNVGsEU0QgNT+MW9z7abTYZh8Opvf86ejesAsxX/3G//AGdqGn5fI4HGBvx+P8FAgIb6Bjbv2kVMs8BJmliENOiZnYnL7kBKg5K9+6gLBJFNo4uQBm4Bw4cMZvz08/B4u3ZWwI3zP+ezZ57EX1dLVq8+XPi9n5KUmRVvszoM7SEYJ22SEkI4gC8Ae9P+r0sp7z1k+z3AI0C6lLLyGOV3AA1ADIi29QUpujZn5p/JW5e+xVPL/h9QjyUqiDnNN81Iuk4E6D+ibYaxVu7awex//YWqkl0gJVKaz3zdaiUSDOJOSqb3mPGcMeNSkrNPnGApHApSV1lJ6e5dJKWmUbG3lIDfT/8hQynfvYs3Z39ATMC2vWUgzOvTYlHSEtykpaWTm5/PoBEjSU7PaJNr7Yj0n3gmfcZOYOP8L/j4yX/x0ZP/5KpfdZ3oAJ2B5vRhhIBpUkqfEMIKzBNCvC+lXCiEyAemA7tOcoyzjiUmCsWpkOpM5aY77uXFX/6I8y/5Jn3HTsQwYkRCIWr3ltJQWcHMv/+RfVs3E/L5CIeCICVjLruKiVffgBGNsubTD9kw/3PKt29DSsmw6Rcw9aZbjzpXOOhn7WcfU7xsCTvXrADAmeDFYrebqUWjMQK+etLye3DjQ4+iNdO5zGZ3kJ6bR3puHgA9+hxM/Zqem8fyLdsIBoPccO01NDY2YsRipGVmoXXzkBi6xcqgM8+mrryMr15/kb1bN5Hdu1+8zeo2nPTXLc02K1/T4v7cmvvbsf4K/AR4p02sUyiOw6dP/xuAuc88ydxnnjz2TkJgtduxWK2EAwEWvvEyKz6YSdjvR0oDAE23YMSiLJv1NstmvU1iZjY5ffrhSU1j65KvqCndc8jhBIOnncu5t7ddatgjcXoScHqO3RfSncko7AnAwjde5ms/vfckeytai2a9DgkhdGAZ0Bv4l5RykRDiEmCPlHLVSYa7SeBDIYQE/iOlfOI457gduB2goKCgBZeg6G7EolH2bduKJzmF9KJeB4ZbCiGwOV2k5uaT3bsv+YOHHdgW8jfy2v2/pGLnDlxJSfQeNZZRF19OUmY2i95+jfmvPI/T66WubC91ZXsPnCsxI5O+4yczZNq5JGflxOV6FUfTY8hw8gcOYfuKZXz1+ksMmXYunhTlEd7WNEswpJQxYLgQIgl4SwgxFPgl0Jze5YlSylIhRAbwkRBio5Tyi2Oc4wngCTA7vZt7AYruh2iqOeQNHMKF3/1xs8rYXW5u+MPfjrlt7GVXMfayqwAINjaycs5MElJSyR88DG9aemuZrWhFrA4HF/3gZ8x5/G8seO0FFrz2At70TC6955cHah+K1qdF49KklLXAXOBSoAhY1dSpnQcsF0IcNWRBSlna9FkOvAWowDqK02Luc08RCQXpMWR4qx/b4XYz7vKvM2jqOUosOjgubyJf++m9fPOv/yazZ2/qK8qY+egf421Wl6Y5o6TSgYiUslYI4QTOAR6WUmYcss8OYNSRHdtCCDegSSkbmubPBX7Xmheg6H7sWrMKgN3r16BbrUQjYSLBEJFQkEgwSDjoJxwIoOk66T2KGDh5GnaXcvbqqqTk5JHVux9lxVuxORwYRgytHcOydCea0ySVDTzT1I+hAa9KKWceb2chRA7wlJRyBpCJ2YS1/1wvSik/OH2zFd2ZS3/8K5bNeof1X3zK+i8+PWybEBo2pxOb00U0EmbNJ3NYNuttxl1+DRmFPUnNK0BXYbK7HPkDh7Dmkw8oK97Kf797G8Omz2D4uTOUV3gr05xRUquBESfZp/CQ+VJgRtN8MTDs9ExUKA4nOSuHc275NlOuv5n6inJsDicWux2rw4HFajss5lDJ+rXM+uefmPP43wCwWG2kFxaR2bMPBUOG0XPEaCUgXYB+4yfRe/Q4ti1bxMoPZvLli//Hqo/eZ/TFlzP4rOlYbB0nXEpnRt0pik6LzeEkLf/EYTDyBg7m1r8/RW3ZXsp3FFO2bQtlxVtZ9/knZud2ajpf++lvSO9R1E5WK9oK3WKh79iJ9B07ka1LF7HorVf45OnHmf/aC/QaOYbMnr3IKOyF1eEgHPAT8vtxJyWT2bO3CmzYTJRgKLo8usVCam4+qbn5DJh4JgBGLMb2lct4508P8PYj9zP07PNxeBKwORzYXG7SexTiTes+XtRdjd6jxtLrjDHsXreGlR/OZPvKpaz7/ONj7pvbfyATrrqegsGqMeRkKMFQdEs0XafXGWO4+Ac/44sX/se8l4+IPSUEPUeMYvBZ0+kxdAQ2hzM+hipOGSEEBYOHUjB4KAB15fuoLt1DJBjA7vJgczrZvX4NKz54j9fu/yVnXHgZZ97wLRXU8AQowVB0a/qMmUCfMRMIBwOEA+YUavRRvGIJaz6ZQ/HyJVhsdnqOHE2/CZMpGjEKq+3o0OqKjk9iRhaJGYeP/M/u04+RF1zCWw/fx7JZb7P+y88YMPFMBk6ZRsYhTqEKEyUYCgVmf4jN4YSmpH7Zffox7vJr2LNxHZsXLWDLovlsXjgPR4KX6bd9h96jx6mhm10Ei83GFb+4n00LvmDTwvms+vh9lr//LukFhQyaOp0h06ar0VZNKMFQKI6DbrFQMHgYBYOHMe3m29m9bg2fv/A07/3lD2i6BU9KKnaXC5vThd3lwpOSSo8hw8ko6k1ieoZq2uhEaLrOgMlnMWDyWQR9PjYu+IJ1cz9i7rNPsuC1F/h///dqvE3sECjBUCiagabr9Bg6nOsG/JniZYsoK96Kr7qKUMBP2O/HV11NyYZ1rP7YdDNyeBLIHzSEwWdNp2j4KNW00YlweDwMP9f045j5t4fZ9NWXvHzvTxk45SyGnHVut34RUIKhULQAi9VK33GT6Dtu0lHbYtEo5du3UbFrO6WbNrJj9XK2LFpA33GTOPeO/4fd5Y6DxYrTYcZ37yGzZ2/WfPohHz3xzwPh1bsrSjAUilZCt1jI7tOP7D79GHr2+cSiUZbOfIv5rzxHWfEWLvzeT1Tuhk6GpumMvuQKRl18OY/dci0fPPZXIsEgw6Zf0C1rGt3vihWKdkK3WBh72VVc89uHMQyDl3/zEz79338o3bwRaRjxNk/RAoQQTLj6epIys/nk6cf5vx/dxYoP3iPo8528cBeiS+X0vu+++ygoKFA5vRUdjlDAz5K3XmP3xnXIWAxnYhKN3mwMzYrVbqdo4ACGDO6P22lHSsl7771HMBThzHNm0LMol2SPGqXTEZBSsmnBFyyd+TZlxVtACLJ79SV3wCBy+vYno7DnUUN324v2yOnd5QRDoegq6I31uHZtJiysNHpzSMzIxJGcTu9+vZg2fSp2u4qPFE92r1vN7vVr2LF6BeXFW4lFowAMnDKNC77zw3a3pz0Eo0v1YYwfPx6v10taWlq8TVEoTsjcTeX834IdfP+cPhQl2Vi7ZgNlO3cQqKtBs7twpWaQ03coxpBxVGzZQGBvKYHiNWixAOsXw9JX/od32CQGDB7A5CnjcKrw7e1O/qCh5A0cQu/R41n3+Scsn21mqq7eszvOlrUdXaqGoVB0FgLhGAN+8wHnDszkiZua91IopaSytoE5H33J5k9mk1i7E4Cwbqfw3Cs5c8oYcnr2Yu36LWzYvJNJk8eSnarygbcVdeX7ePV3v6C+ohyhaeQPGkrv0ePoP/HMuORhV01SCkUX4IvNFbywaCdJThtWiyBmSCIxyevLSgD481XDuOKMvBYft3j3Xr6cv4K97zyJ1YgAEHIkYgvWIYB6q5fEkVMZNmoEHpedSCRGIBimrq6OhgYfQX+AaCgI0TBCGgibA4fLjTvBQ2JSIsOHDyI7I6VZtsRiBrt27WFvRQ1Ol5OMFC8ZaclYu2BY8a1LF7HwjZcoK94KwKCp53DmDd/CmeCNq11KMBSKTszqklpeWryLlxabTRSpbvPhqWkCiyZoDEXRNMF/bjiDsT1TT/k8DT4f7364gE3vv4kW9qMXDqZv70K2fPkxzrrS07oGny0Rze5EWmygW8FiRQiBlGBIAxloRAs24Ij4sMjY0bbpbnKv/Da3XT71tOw4FvufXe3pFFmzdw/P/vS7JKSkMeTs8+g7diKJGZntdv4ToQRDoehkBCMxPt5QxjsrS/lofRkAIwuSuGxELjeNL2x3e9Zu2Mr2bTtoDISxWnQcdiuJiV6SkxPxJrhxu51Y7Q6k0Aj6/dTW1lNb10BFeSXbNmygpmQnoUAALRZBxCJosSgSEIAQIG0udE8izsRkvJnZJKckEw4Fqa/34auvR1sxB7/upP/Vd3L1ZQcd3jZsKmbux5+T1aOQ2vJ9lJeUENNtuNIyGTakP+NHDcJm0dm+fTer1m1l784d1O7ZRbSmDBH0o0cC2GIhIrqNHhdcw/U3Xtnq/zspJY21NUTDYXauXs7mhfPYtXY1mq5z858fIzk7t9XPeToowVAoOgnrS+t5ecku3lqxh4ZglIwEO5cMy+G2KT3J9DribV7cePGtj9n6zrM4AzVE+k/CbtXx1dbg2b3qsP1iQkOXB31TYmjEhI5NRg6sC1hchNzpaC4PVpcbYXcRKtlKYn0JZBbS97zLmT59Mg6btdn2VezawWf/9wSxaJTMol5U7NxOXUUZDk8CvuoqAvV1B/ZNzs6l77iJ9B03iYzCnqfxX2kblGAoFB2YioYQC7ZV8uxXO1m2swabRWPG4CyuGpXPuJ6p6JqKHwXQ0NDAo7+8F2fZZqJCJ2x1E3OnMPz8i3C6XGRlZzJoYB8wYuzesZNFS9exa9s2tFgYV3o2/Qb2Y0C/XuRkpx917MZAmL98925c9WbTm093Q68RDB06AN3uBE3HbrMi0di3Yxv1NbX4IwY5jbsINdRRX1F+4FgWq43U/AJScvMJNfpwJnjxpmeiW630HDmatPweHTommBIMhaID8v6avfzpw01sq2gEID/FyTfGF3LlGXkkubpeJ29r4Wv0Y7fbsVpaNyx8LBZl05adfPDBXErXrSSrfjvHe6wbCDQk1ow8iop6kDdoCDl9+pNeWNTpw9UrPwyFooNhGJLfz95ASU2An13Qn7FFKQzNS1K1iWbgcbeNr4iuWxjYvxcD+/cCYP2OMvaVVUAkjJAxgqEwMholJy8H6U3jin99yd+uG8XFw3LaxJ6ujBIMhaIFfLKxnJKaAAOyvdx5Zq94m6M4BgMLMxlYeOyRS1vKGjCEftwaiOLEqOCDCkULGN8rlQS7hXA0Rih69DBSRcdmfwN8B+6K6NAowVAoWoDHbuHv141gW0Ujj322Ld7mKFrI/i5boeoYp4QSDIWihZzVL4NLhuXw+Ofb2F3tj7c5ihYg2e/sF2dDOilKMBSKU+DnM/qjC8GDszfE2xRFCzhYw1CcCqrTW6E4BbITndw1tRd//mgzl/5rPpo4+BASQhwyf0jzxyEf4sC8ODh/yL6HvgHvP54Q+8seXNaEwOMwb2MzXIfEaPrkwLK5TkrTe3n/8tdG5HLZiI7lrdzWHBAMpRinhBIMheIUuW1KT0rrApTUBICDD6P9zR5SHr7u4Dzsd2qWGAf3bTqulPKQ+ab1TevMZXng2DFDUu0PY9UEFl1DaxIRmj73L+8XGU0zl1eX1LGr2t/9BONgt3dc7eisKMFQKE4Rh1XnD5cPjbcZp0Thz2axvbIx3ma0O6qGcXoowVAouiF9MjzdOsaV0otTQ3V6KxTdEKdNx6p338dmR44J1ZFRNQyFopvyVXEVUx/5rNWP29oP49Y8WihqtPoxuxNKMBSKbsgtk4r4dGP5yXdsAW0Rx7QtQqNO7J3KyB7JbXDkro8SDIWiG3Lp8FwuHd69RkgpTh/Vh6FQKBSKZnFSwRBCOIQQi4UQq4QQ64QQvz1i+z1CCCmESDtO+fOFEJuEEFuFED9rLcMVCoVC0b40p4YRAqZJKYcBw4HzhRDjAIQQ+cB0YNexCgohdOBfwAXAQOBaIcTAVrBboVAoFO3MSQVDmviaFq1N0/6+qL8CP+H4fVNjgK1SymIpZRh4Gbj09ExWKBQKRTxoVh+GEEIXQqwEyoGPpJSLhBCXAHuklKtOUDQX2H3IcknTumOd43YhxFIhxNKKiormWa9QKBSKdqNZgiGljEkphwN5wBghxFDgl8BvTlL0WMOdj1kbkVI+IaUcJaUclZ5+dLJ3hUKhUMSXFo2SklLWAnMxm5WKgFVCiB2YQrJcCJF1RJESIP+Q5Tyg9BRtVSgUCkUcac4oqXQhRFLTvBM4B1ghpcyQUhZKKQsxhWGklHLfEcWXAH2EEEVCCBtwDfBua16AQqFQKNqH5jjuZQPPNI140oBXpZQzj7ezECIHeEpKOUNKGRVC3A3MAXTgaSnlupOdcNmyZZVCiJ3Nu4SjSAMqT7Fse9CR7evItoGy73RR9p0eHdm+NKBHW59EyLbw548jQoilUspR8bbjeHRk+zqybaDsO12UfadHR7avvWxTnt4KhUKhaBZKMBQKhULRLLqiYDwRbwNOQke2ryPbBsq+00XZd3p0ZPvaxbYu14ehUCgUirahK9YwFAqFQtEGKMFQKBQKRbPoFIIhhLiqKbS6IYQYdcj6VCHEZ0IInxDin0eUmdsUVn1l05RxnGP/vCn0+iYhxHntZd8h+7wrhFh7nG1WIcQzQog1QogNQoifdyT7mrYPFUJ81XT8NUIIR0eyr2mfgqZj3NNS29rSPiHEdCHEsqb/2zIhxLSOYlvT9rjcG0KID8TBdAr/bvIBO/K4cbs3mmNf035xuTeaa1/Tvi26NzqFYABrgcuBL45YHwR+DRzvYq+XUg5vmo7KRynMUOvXAIOA84HHTvTPbW37hBCXA75jbWviKsAupRwCnAHcIYQo7Cj2CSEswPPAnVLKQcBUINJR7DuEvwLvn4JdbW1fJXBx0/f7DeC5jmJbnO+Nq5vSKQwG0jHvgyOJ571xUvvifG805/+3nxbdG51CMKSUG6SUm46xvlFKOQ/zn3cqXAq8LKUMSSm3A1sxQ7K3uX1CCA/wQ+CBEx0acDf9+JxAGKjvQPadC6zeH7FYSlklpYx1IPsQQlwGFAMnjTDQ3vZJKVdIKffHVlsHOIQQ9o5gG3G8N6SU+3/jFsDGsQOWxu3eaKZ9cbs3mmnfKd0bnUIwToP/CbM56tdCiGNFzm12+PU24H7gz4D/BPu8DjQCezGTVP1JSlndDrZB8+zrC0ghxBwhxHIhxE/axzSgGfYJIdzAT4HfHm+fNqQ5/79DuQIzRluo7Uw6QHNsi+e9gRBiDmY6hQbM++BI4nlvNMe+eN4bJ7XvVO+N5sSSaheEEB8DR0a7BfillPKdUzjk9VLKPUKIBOAN4Ebg2SNPe4xyx1PjVrNPCDEc6C2l/MFJqtFjgBiQAyQDXwohPpZSFncQ+yzAJGA05sPnEyHEMinlJx3Evt8Cf5VS+o79vhB3+/bvPwh4GPOttKPYFpd748CJpDyvqc3/BWAa8NERu8Tl3miBfXG5N1pgX7PvjUPpMIIhpTynlY+3p+mzQQjxIuYP7EjBaHb49Va2bzxwhjBDw1uADCHEXCnl1CP2uw74QEoZAcqFEPOBUZjVyI5gXwnwuZSyEkAIMRsYCRx1U8TJvrHAlUKIPwJJgCGECEopj+oEjpN9CCHygLeAm6SU2451sDh+t/G4Nw49blAI8S5m89iRD7x43RvNtS9e90Zz7Wv2vXEoXbJJSghhEUKkNc1bgYswO4+O5F3gGiGEXQhRBPQBFre1fVLKx6WUOdIMDT8J2HyshwlmVXuaMHED44CNHci+OcBQIYSrqS35TGB9R7FPSjlZHgzB/zfgwZPdEO1pnzDTBswCfi6lnN/WdrXENuJ0bwghPEKI7KZ5CzCDY//m43JvtMC+uNwbzbXvlO8NKWWHn4CvYSp2CCgD5hyybQdQjTniowQYCLiBZcBqzA6dRwG9af9LgN8dUv6XwDZgE3BBe9h3RNlCYO0hywfsAzzAa03XsB74cUeyr2n5hib71gJ/7Gj2HbL+PuCejmQf8CvMdviVh0wZHcG2eN0bQCZmHp399+4/AEtHuTeaa1+87o2W2Hcq94YKDaJQKBSKZtElm6QUCoVC0foowVAoFApFs1CCoVAoFIpmoQRDoVAoFM1CCYZCoVAomoUSDIVCoVA0CyUYCoVCoWgW/x+zIr+j6BTMmAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#clipPoly block 2 of 4\n",
    "#visualize the to-be-cut precincts, just to confirm there are really xx precincts up there\n",
    "for p in cutPrecinctList:\n",
    "    if vtdGeom[p].centroid.y > 34 :\n",
    "        plotPoly(vtdGeom[p])\n",
    "plotPoly(clipPoly)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "0218d8e6-7832-479f-b44f-b37c117ee40c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#clipPoly block 3 of 4\n",
    "#now visualize the receivers\n",
    "for pp in range(len(precinctReceivers)):\n",
    "    p = precinctReceivers[pp]\n",
    "    plotPoly(vtdGeom[p])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "5d698e51-7b2d-40b8-b932-d22f20f09bb0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#now visualize the TRACT receivers\n",
    "for tt in range(len(tractReceivers)):\n",
    "    t = tractReceivers[tt]\n",
    "    plotPoly(tractGeom[t])\n",
    "    plt.text(tractGeom[t].centroid.x,tractGeom[t].centroid.y,t)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "1e3b2030-6917-4fa5-9b57-dfff8e7da555",
   "metadata": {},
   "outputs": [],
   "source": [
    "tractReceivers = [1743,774,1579]  #overrule the defaults here"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "c17c1582-29d1-41bc-8f56-b32ac70747ee",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I have finished slicing  141628.0 people (VAP of 120387.0 ) and  88411.0 voters out.\n"
     ]
    }
   ],
   "source": [
    "#now, cut and redistribute\n",
    "cutPop = 0.\n",
    "cutVAP = 0.\n",
    "cutHisp = 0.\n",
    "cutBlack = 0.\n",
    "cutTrump = 0.\n",
    "cutBiden = 0.\n",
    "\n",
    "for ct in range(len(cutTractList)):\n",
    "    t = cutTractList[ct]\n",
    "    cutPop += tractPop[t]  #sum up the cut pops\n",
    "    cutVAP += tractVAP[t]\n",
    "    cutHisp += tractHisp[t]\n",
    "    cutBlack += tractBlack[t]\n",
    "    # Now we zero out the pops in the cut tracts\n",
    "    tractPop[t] = 0\n",
    "    tractVAP[t] = 0\n",
    "    tractHisp[t] = 0\n",
    "    tractBlack[t] = 0\n",
    "# Now distribute the pops among the receivers\n",
    "ntR = len(tractReceivers)\n",
    "NTR = float(ntR)\n",
    "for rt in range(ntR) :\n",
    "    t = tractReceivers[rt]\n",
    "    tractPop[t] += cutPop/NTR\n",
    "    tractVAP[t] += cutVAP/NTR\n",
    "    tractHisp[t] += cutHisp/NTR\n",
    "    tractBlack[t] += cutBlack/NTR\n",
    "\n",
    "#             now do the same for the precincts\n",
    "for cp in range(len(cutPrecinctList)):\n",
    "    p = cutPrecinctList[cp]\n",
    "    cutTrump += vtdTrump[p]\n",
    "    cutBiden += vtdBiden[p]\n",
    "        # Now we zero out the pops in the cut precincts\n",
    "    vtdTrump[p] = 0\n",
    "    vtdBiden[p] = 0\n",
    "\n",
    "# Now distribute the pops among the receivers\n",
    "npR = len(precinctReceivers)\n",
    "NPR = float(npR)\n",
    "for rp in range(npR) :\n",
    "    p = precinctReceivers[rp]\n",
    "    vtdTrump[p] += cutTrump/NPR\n",
    "    vtdBiden[p] += cutBiden/NPR\n",
    "print(\"I have finished slicing \",cutPop,\"people (VAP of\",cutVAP,\") and \",cutBiden+cutTrump,\"voters out.\" )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "55419803-eaaf-4181-8848-d8fdad0205b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Now, repeat for southern central tip\n",
    "x,y = tractMAP.exterior.xy\n",
    "plt.plot(x,y)\n",
    "pt1 = Point(-76.2,37.8)\n",
    "pt2 = Point(-76.4,38.6)\n",
    "pt3 = Point(-77.4,38.62)\n",
    "pt4 = Point(-78, 37.8)\n",
    "clipPoly = Polygon([pt1,pt2,pt3,pt4])  #LEFTOVER FROM INDIANA. plan to clip off SW corner near Evansville\n",
    "xc,yc = clipPoly.exterior.xy\n",
    "plt.plot(xc,yc)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "2a56b3e5-803d-425d-8bb6-abfba9aa9716",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I have flagged  142 tracts w pop 263227.0  to reassign to tracts...\n",
      "[19, 20, 22, 23, 29, 30, 344, 345, 458, 648, 651, 652, 688, 689, 690, 709, 710, 712, 713, 715, 717, 718, 732, 764, 767, 885, 887, 941, 942, 952, 964, 965, 986, 990, 992, 993, 994, 996, 1000, 1446, 1447, 1448, 1495, 1496, 2093, 2156, 2777, 2779, 2872, 2873, 2874, 2895, 3144, 3246, 3287, 3288, 3438, 3441, 4029]\n",
      "I have flagged  75 precincts to reassign to precincts...\n",
      "[297, 300, 340, 347, 348, 349, 368, 433, 436, 481, 526, 546, 757, 759, 760, 762, 773, 775, 776, 777, 778, 783, 784, 786, 788, 1669, 1671, 1672, 1676, 1677, 1678, 1680, 1689, 1700]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#find all tracts and precincts centered in this clipPoly, flag for cutting\n",
    "# and, ID all nearby tracts and precincts to receive their pops and voters\n",
    "cutTractList = [-99999]\n",
    "cutPrecinctList = [-99999]\n",
    "tractReceivers = [-88888]\n",
    "precinctReceivers = [-88888]\n",
    "popToCut = 0.\n",
    "for t in range(nTracts):\n",
    "    x = tractGeom[t].centroid.x\n",
    "    y = tractGeom[t].centroid.y\n",
    "    if tractGeom[t].intersects(clipPoly) :\n",
    "        CP = Point(x,y)\n",
    "        if CP.intersects(clipPoly):\n",
    "            isSkippedTract[t] = 1\n",
    "            popToCut += tractPop[t]\n",
    "            if cutTractList == [-99999]:\n",
    "                cutTractList = [t]\n",
    "            else:\n",
    "                cutTractList.append(t)\n",
    "            x,y = tractGeom[t].exterior.xy\n",
    "            plt.plot(x,y)\n",
    "    else:\n",
    "        if tractGeom[t].distance(clipPoly) < 0.1 and tractGeom[t].centroid.x < -76.5:\n",
    "            if tractReceivers == [-88888]:\n",
    "                tractReceivers = [t]\n",
    "            else:\n",
    "                tractReceivers.append(t)\n",
    "xp,yp = clipPoly.exterior.xy\n",
    "plt.plot(xp,yp)\n",
    "\n",
    "for p in range(nPrecincts):\n",
    "    x = vtdGeom[p].centroid.x\n",
    "    y = vtdGeom[p].centroid.y\n",
    "    if vtdGeom[p].intersects(clipPoly) :\n",
    "        CP = Point(x,y)\n",
    "        if CP.intersects(clipPoly):\n",
    "            isSkippedPrecinct[p] = 1\n",
    "            if cutPrecinctList == [-99999]:\n",
    "                cutPrecinctList = [p]\n",
    "            else:\n",
    "                cutPrecinctList.append(p)\n",
    "    else:\n",
    "        if vtdGeom[p].distance(clipPoly) < 0.1 and vtdGeom[p].centroid.x < -76.5:\n",
    "            if precinctReceivers == [-88888]:\n",
    "                precinctReceivers = [p]\n",
    "            else:\n",
    "                precinctReceivers.append(p)\n",
    "print(\"I have flagged \",len(cutTractList),\"tracts w pop\",popToCut,\" to reassign to tracts...\")\n",
    "print(tractReceivers)\n",
    "print(\"I have flagged \",len(cutPrecinctList),\"precincts to reassign to precincts...\")\n",
    "print(precinctReceivers)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "264c0a29-0366-433f-ae45-63b986566b34",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#clipPoly block 2 of 4\n",
    "#visualize the to-be-cut precincts, just to confirm there are really xx precincts up there\n",
    "for p in range(nPrecincts):\n",
    "    if isSkippedPrecinct[p] ==1:\n",
    "        if notPolyVTD[p]==1:\n",
    "            for geom in vtdGeom[p].geoms:\n",
    "                x,y = geom.exterior.xy\n",
    "                plt.plot(x,y)\n",
    "        else:\n",
    "            x,y = vtdGeom[p].exterior.xy\n",
    "            plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "34f94200-4cc7-4226-9a6c-d6ae75b3380f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#clipPoly block 3 of 4\n",
    "#now visualize the receivers\n",
    "for pp in range(len(precinctReceivers)):\n",
    "    p = precinctReceivers[pp]\n",
    "    if notPolyVTD[p]==1:\n",
    "        for geom in vtdGeom[p].geoms:\n",
    "            x,y = geom.exterior.xy\n",
    "            plt.plot(x,y)\n",
    "    else:\n",
    "        x,y = vtdGeom[p].exterior.xy\n",
    "        plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "9301c018-ac66-4a64-8efe-a839a349e53f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#now visualize the TRACT receivers\n",
    "for tt in range(len(tractReceivers)):\n",
    "    t = tractReceivers[tt]\n",
    "    if notPoly[t]==1:\n",
    "        for geom in tractGeom[t].geoms:\n",
    "            x,y = geom.exterior.xy\n",
    "            plt.plot(x,y)\n",
    "    else:\n",
    "        x,y = tractGeom[t].exterior.xy\n",
    "        plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "e8d4b939-2d62-4d7a-a866-7283b3e8e383",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I have finished slicing  263227.0 people (VAP of 201461.0 ) and  133760.0 voters out.\n"
     ]
    }
   ],
   "source": [
    "#now, cut and redistribute\n",
    "cutPop = 0.\n",
    "cutVAP = 0.\n",
    "cutHisp = 0.\n",
    "cutBlack = 0.\n",
    "cutTrump = 0.\n",
    "cutBiden = 0.\n",
    "\n",
    "for ct in range(len(cutTractList)):\n",
    "    t = cutTractList[ct]\n",
    "    cutPop += tractPop[t]  #sum up the cut pops\n",
    "    cutVAP += tractVAP[t]\n",
    "    cutHisp += tractHisp[t]\n",
    "    cutBlack += tractBlack[t]\n",
    "    # Now we zero out the pops in the cut tracts\n",
    "    tractPop[t] = 0\n",
    "    tractVAP[t] = 0\n",
    "    tractHisp[t] = 0\n",
    "    tractBlack[t] = 0\n",
    "# Now distribute the pops among the receivers\n",
    "ntR = len(tractReceivers)\n",
    "NTR = float(ntR)\n",
    "for rt in range(ntR) :\n",
    "    t = tractReceivers[rt]\n",
    "    tractPop[t] += cutPop/NTR\n",
    "    tractVAP[t] += cutVAP/NTR\n",
    "    tractHisp[t] += cutHisp/NTR\n",
    "    tractBlack[t] += cutBlack/NTR\n",
    "\n",
    "#             now do the same for the precincts\n",
    "for cp in range(len(cutPrecinctList)):\n",
    "    p = cutPrecinctList[cp]\n",
    "    cutTrump += vtdTrump[p]\n",
    "    cutBiden += vtdBiden[p]\n",
    "        # Now we zero out the pops in the cut precincts\n",
    "    vtdTrump[p] = 0\n",
    "    vtdBiden[p] = 0\n",
    "\n",
    "# Now distribute the pops among the receivers\n",
    "npR = len(precinctReceivers)\n",
    "NPR = float(npR)\n",
    "for rp in range(npR) :\n",
    "    p = precinctReceivers[rp]\n",
    "    vtdTrump[p] += cutTrump/NPR\n",
    "    vtdBiden[p] += cutBiden/NPR\n",
    "print(\"I have finished slicing \",cutPop,\"people (VAP of\",cutVAP,\") and \",cutBiden+cutTrump,\"voters out.\" )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "84c2cf09-b3ea-4fc2-a55b-62985882836b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on tract 200\n",
      "working on tract 400\n",
      "working on tract 600\n",
      "working on tract 800\n",
      "working on tract 1000\n",
      "working on tract 1200\n",
      "working on tract 1400\n",
      "working on tract 1600\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#rebuild tractMAP, excluding skipped / sliced tracts.  #NOT NEEDED FOR OHIO -- NO SLICED populous TRACTS\n",
    "counter = -1 #Watchout - low-number tracts may have been skipped\n",
    "found = \"no\"\n",
    "while found == \"no\":\n",
    "    counter +=1\n",
    "    if isSkippedTract[counter]==0:\n",
    "        starter = counter\n",
    "        found = \"yes\"\n",
    "\n",
    "tractMAP = tractGeom[starter]\n",
    "for t in range(starter+1,nTracts):\n",
    "    if t%200 == 0:\n",
    "        print(\"working on tract\",t)\n",
    "    if isSkippedTract[t] == 0:\n",
    "        tractMAP = tractMAP.union(tractGeom[t])\n",
    "\n",
    "plotPoly(tractMAP)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "650cf37d-e11a-4ed3-861b-3f3edc411422",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#What is our distribution of precinct's Trump votes by precinct Biden votes?\n",
    "fig, ax = plt.subplots()\n",
    "ax.set(xlabel=\"Biden votes in this precinct\", ylabel=\"Trump precinct votes\")\n",
    "plt.plot(vtdBiden, vtdTrump, marker='.',linestyle=\"none\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "d40408f3-31eb-4b8a-ad91-4e2eb5f422df",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 0.7506631299734748 377\n",
      "100 0.7911802853437094 771\n",
      "200 0.38528452337803554 2759\n",
      "300 0.5792746113989637 965\n",
      "400 0.6276551436901291 2401\n",
      "500 0.4418604651162791 1505\n",
      "600 0.4297924297924298 2457\n",
      "700 0.39593908629441626 3152\n",
      "800 0.6755 4000\n",
      "900 0.4741532976827095 1683\n",
      "1000 0.693089430894309 5412\n",
      "1100 0.25859658932065976 3577\n",
      "1200 0.5552905094564662 5869\n",
      "1300 0.3320568927789934 1828\n",
      "1400 0.5013850415512465 1444\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#ALL TRIMS COMPLETE. let's convert the partisan data to the format we use for HD code\n",
    "# in this prototype, we only use 2020 presidential data, but this could be modified\n",
    "vtdGOP = [0.]*nPrecincts  #Each precinct's R/(R+D)\n",
    "vtdPop = [0.]*nPrecincts   #THIS IS VOTER POPULATION\n",
    "vtdArea = [0.]*nPrecincts\n",
    "vtdCPx = [0.]*nPrecincts   #these are centroid x and y of each precinct\n",
    "vtdCPy = [0.]*nPrecincts\n",
    "plotcolor = ['blue']*nPrecincts\n",
    "for p in range(nPrecincts):\n",
    "    vtdPop[p] = vtdTrump[p] + vtdBiden[p] \n",
    "    vtdGOP[p] = vtdTrump[p]/max((vtdTrump[p] + vtdBiden[p]), 0.01)  #avoid divide by zero\n",
    "    if (vtdGOP[p] > 0.40) :\n",
    "        plotcolor[p]='purple'\n",
    "        if (vtdGOP[p] > 0.60) :\n",
    "            plotcolor[p] = 'red'\n",
    "    vtdArea[p] = vtdGeom[p].area\n",
    "    vtdCPx[p] = vtdGeom[p].centroid.x  #not needed except for graphing\n",
    "    vtdCPy[p] = vtdGeom[p].centroid.y   #not needed  \"  \"  \"\n",
    "    if isSkippedPrecinct[p] == 0 and p%10 == 0: #memory drain if we print all\n",
    "        plt.scatter(vtdCPx[p], vtdCPy[p], marker='.',c=plotcolor[p])\n",
    "for p in range (nPrecincts):\n",
    "    if p%100 == 0:\n",
    "        print(p,vtdGOP[p],vtdPop[p])\n",
    "plotPoly(wholeMAP)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "a1e3f0d2-c21c-46d4-8227-e75dfd5e89b6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "original and final map areas are 28.917763863406417 28.120058780387055\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "MAP = tractMAP  #committing to the trimmed map (for TN, using convex hull)\n",
    "#bufferDistance = 0.02  #do minor buffer for OH\n",
    "origMAP = wholeMAP    # let's keep a copy of the original map prior to buffering\n",
    "#MAPbuffer = MAP.exterior.buffer(bufferDistance, single_sided=True)  #gives a little exterior buffer\n",
    "# MAP = origMAP.convex_hull  #alternate to buffer for weird state shapes\n",
    "bBox = MAP.bounds\n",
    "MAPMinX = bBox[0]  #these four are useful for slicing ops, and are basis for map grid (without buffer)\n",
    "MAPMinY = bBox[1]\n",
    "MAPMaxX = bBox[2]\n",
    "MAPMaxY = bBox[3]\n",
    "\n",
    "#MAP = MAP.union(MAPbuffer)  #not buffering for TN\n",
    "print(\"original and final map areas are\",origMAP.area,MAP.area)  #same if we didn't buffer\n",
    "plotPoly(tractMAP)\n",
    "plotPoly(origMAP)\n",
    "plt.show()\n",
    "\n",
    "minTractPop = 10  #was zero to not exclude empty interiors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "8e271f55-e118-4575-bc99-c28c59f435b7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2020 population, Trump+Biden voters, lean =  5893715 3240940 0.4968093673346601\n",
      "compare to Census 5893718 Leip has Trump + Biden = 3241050.0 0.4968093673346601\n"
     ]
    }
   ],
   "source": [
    "#let's confirm some population stats - these match 2020 census, USelectionatlas.org :-)  #NOTE, did not confirm here for AZ, have done before\n",
    "censusPop = 5893718\n",
    "atlasTrump = 1610184.\n",
    "atlasBiden = 1630866\n",
    "\n",
    "print(\"2020 population, Trump+Biden voters, lean = \",np.sum(tractPop),np.sum(vtdPop),stateGOP )\n",
    "print(\"compare to Census\",censusPop,\"Leip has Trump + Biden =\",atlasTrump+atlasBiden,\n",
    "      atlasTrump/(atlasTrump+atlasBiden) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "6c47f717-f208-4a4c-b825-a3181a83c6ee",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "state pop before, after slice operation are 7151500 7151500.0\n",
      "state Trumpers before, after slice opn are 1661682 1661682.0\n",
      "state Bideners before, after slice opn are 1672134 1672134.0\n"
     ]
    }
   ],
   "source": [
    "#Check on integrity after slicing\n",
    "sumPop = 0.\n",
    "sumTrump = 0.\n",
    "sumBiden = 0.\n",
    "for t in range(nTracts):\n",
    "    if isSkippedTract[t] == 0:\n",
    "        sumPop += tractPop[t]\n",
    "for p in range(nPrecincts):\n",
    "    if isSkippedPrecinct[p] == 0:\n",
    "        sumTrump += vtdTrump[p]\n",
    "        sumBiden += vtdBiden[p]\n",
    "print(\"state pop before, after slice operation are\",np.sum(tractPop),sumPop)\n",
    "print(\"state Trumpers before, after slice opn are\",np.sum(vtdTrump),sumTrump)\n",
    "print(\"state Bideners before, after slice opn are\",np.sum(vtdBiden),sumBiden)\n",
    "for t in range(nTracts):\n",
    "    if isSkippedTract[t] ==1 and tractPop[t] > 0:  #should not occur\n",
    "        print(\"missed pop for tract, pop, (x,y)\",t,tractPop[t],tractGeom[t].centroid.x,\n",
    "              tractGeom[t].centroid.y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "2f6c72cd-90d8-489d-9b15-d4278ba17b3d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "map has a total of 49 grids for 9 districts\n",
      "starting grid no 0 at x = -114.3250765, y = 31.737439857142856 \n",
      "starting grid no 5 at x = -114.3250765, y = 35.78842842857142 \n",
      "starting grid no 10 at x = -113.51278350000001, y = 34.168032999999994 \n",
      "starting grid no 15 at x = -112.7004905, y = 32.54763757142857 \n",
      "starting grid no 20 at x = -112.7004905, y = 36.59862614285715 \n",
      "starting grid no 25 at x = -111.88819749999999, y = 34.97823071428571 \n",
      "starting grid no 30 at x = -111.07590450000001, y = 33.35783528571428 \n",
      "starting grid no 35 at x = -110.26361150000001, y = 31.737439857142856 \n",
      "starting grid no 40 at x = -110.26361150000001, y = 35.78842842857142 \n",
      "starting grid no 45 at x = -109.45131849999999, y = 34.168032999999994 \n",
      "done building grids. avgGridDensity, maxGridDensity,nPopulatedGrids = 226387.36327445298 5993812.183886375 48.0\n"
     ]
    }
   ],
   "source": [
    "# this section - ESTIMATE POP'N DENSITY at scale of 1/3 of district length\n",
    "# also, IDENTIFY WHICH TRACTS and VTD's INTERSECT EACH GRID to speed later intersection searches by grid \n",
    "# (above sections already pulled in Tract and Precinct geometry and demographic data, built an overall map)\n",
    "nDistricts = 9   #AZ congressional\n",
    "avgDistrictPop = np.sum(tractPop) / float(nDistricts)\n",
    "#MAPMaxX = -80.   #In most state maps, we figure this out from the bounding box of the convex hull; see above.\n",
    "#MAPMaxY = 31.1   # ... but for Florida, we will do manually\n",
    "#MAPMinX = -87.0\n",
    "#MAPMinY = 25.5\n",
    "stateWidth = float(MAPMaxX - MAPMinX)\n",
    "stateHeight = float(MAPMaxY - MAPMinY)\n",
    "stateWHRatio = stateWidth / stateHeight\n",
    "G = 2.5  #adjustable parameter; G*G = number of grids that fit in an average district\n",
    "nGridsX = int( G*round((nDistricts*stateWHRatio)**0.5,0) )   #OK to have empty grids for a non-rectglr state\n",
    "nGridsY = int(round(nGridsX / stateWHRatio,0) )  #so grids are square even if state has high aspect ratio\n",
    "nGrids = int(nGridsX*nGridsY)\n",
    "print(\"map has a total of {0} grids for {1} districts\".format(nGrids,nDistricts) )\n",
    "nGridPrecincts = [0]*nGrids # will store how many precincts intersect with each grid square\n",
    "nGridTracts = [0]*nGrids    # same, but for tracts\n",
    "gridPrecinctNo = [[0]*nPrecincts for gridNo in range(nGrids) ] # initialize a list of VTDs that intersect with each grid square\n",
    "gridTractNo = [[0]*nTracts for gridNo in range(nGrids) ] # initialize a list of tracts that intersect with each grid square\n",
    "\n",
    "gridPop = [0.]*nGrids #will store approx total population for this grid square\n",
    "gridDensity = [0.]*nGrids\n",
    "gridGeom = [Polygon([(0,0),(0,1),(1,0)])]*nGrids  #initialize grid geometry\n",
    "gridWidth = stateWidth /nGridsX        #geo length of a grid's side length\n",
    "gridHeight = stateHeight /nGridsY    \n",
    "\n",
    "for nG in range (nGrids) : #  create polygon shape for each grid square, compute grid-square population density\n",
    "    x = int(nG/nGridsY)\n",
    "    y = int(nG % nGridsY)\n",
    "    point1 = Point(x*gridWidth+MAPMinX,y*gridHeight+MAPMinY)\n",
    "    point2 = Point((x+1)*gridWidth+MAPMinX, y*gridHeight+MAPMinY)\n",
    "    point3 = Point((x+1)*gridWidth+MAPMinX, (y+1)*gridHeight+MAPMinY)\n",
    "    point4 = Point(x*gridWidth+MAPMinX, (y+1)*gridHeight+MAPMinY)\n",
    "    gridGeom[nG] = Polygon([point1, point2, point3, point4])\n",
    "    if (nG %5 == 0): #print occasionally, should take about one second per grid\n",
    "        print(\"starting grid no {0} at x = {1}, y = {2} \".format(nG,gridGeom[nG].centroid.x,gridGeom[nG].centroid.y) )\n",
    "    counter = 0\n",
    "    #    Now for each grid square, find intersxn with all polygons, total the intersection population\n",
    "    #    Also, create 2 lists: of TRACTS and precincts that intersect each grid (efficiency shortcut)\n",
    "    \n",
    "    if( gridGeom[nG].intersection(MAP).area > 0.0001) : #don't bother w grids off the map\n",
    "        for t in range(nTracts):\n",
    "            # if (t%3000 == 0) :\n",
    "            #    print (\"grid {0}, tract no {1}, ({2},{3})\".format(nG,t,tractGeom[t].centroid.x,tractGeom[t].centroid.y) )\n",
    "            intersxnArea = 0.\n",
    "            if (notPoly[t] == 0) :  # tract is a simple polygon\n",
    "                intersxnArea = gridGeom[nG].intersection(tractGeom[t]).area  #replaced tractGeom w cleanedPoly\n",
    "            else : #tract is a multiPolygon, do the polygon intersections individually\n",
    "                #print(\"I think tract\",t,\"is a multiPolygon\")\n",
    "                for geom in tractGeom[t].geoms :\n",
    "                    intersxnArea += gridGeom[nG].intersection(geom).area\n",
    "            if (intersxnArea > 0 and tractPop[t] > minTractPop) : # this tract is at least partially in this grid and populated \n",
    "                if (tractArea[t] == 0):  #should not happen, but debugging\n",
    "                    print(t,tractGeom[t].centroid.x, tractGeom[t].centroid.y)  #debug print\n",
    "                gridPop[nG] += tractPop[t]*intersxnArea/tractArea[t]\n",
    "                if tractPop[t] > (1.0 * avgDistrictPop) :  #flag if we have a mega tract\n",
    "                    uhoh = input(\"Uh oh! We have a tract that's bigger than a district.  What now?\")\n",
    "                else :\n",
    "                    gridTractNo[nG][counter] = t  #add this tract to this grid's list, update the no of tracts in grid\n",
    "                    counter +=1            \n",
    "                    nGridTracts[nG] = counter\n",
    "        # OK, now, loop over PRECINCTS to develop each grid's list of precincts (prev loop was for tracts)\n",
    "        counter = 0\n",
    "        for p in range(nPrecincts):\n",
    "            intersxnArea = 0.\n",
    "            if (notPolyVTD[p] == 0) :  # precinct is a simple polygon\n",
    "                intersxnArea = gridGeom[nG].intersection(vtdGeom[p]).area  #replaced tractGeom w cleanedPoly\n",
    "            else : #precinct is a multiPolygon, do the polygon intersections individually\n",
    "                for geom in vtdGeom[p].geoms :\n",
    "                    intersxnArea += gridGeom[nG].intersection(geom).area\n",
    "            intersxnArea = gridGeom[nG].intersection(vtdGeom[p]).area\n",
    "            if (intersxnArea > 0 and vtdPop[p] >0) : # this precinct is at least partially in this grid and recorded votes                 \n",
    "                gridPrecinctNo[nG][counter] = p  #add this precinct to this grid's list, update the no of tracts in grid\n",
    "                counter +=1            \n",
    "                nGridPrecincts[nG] = counter\n",
    "    gridDensity[nG] = gridPop[nG] / gridGeom[nG].area  #finished loop over tracts & vtd's.  Calceach grid's population density    \n",
    "\n",
    "minGridPop = np.min(gridPop)\n",
    "# avgGridPop = np.mean(gridPop)   #see below for explicit averaging, since there are so many empty grids\n",
    "maxGridPop = np.max(gridPop)\n",
    "nPopulatedGrids = 0.\n",
    "totPopulatedGridArea = 0.\n",
    "for nG in range(nGrids) :\n",
    "    if gridPop[nG] > 0. :\n",
    "        nPopulatedGrids +=1\n",
    "        totPopulatedGridArea += gridGeom[nG].area\n",
    "avgGridPop = np.sum(tractPop) / nPopulatedGrids\n",
    "avgGridDensity = np.sum(tractPop) / totPopulatedGridArea\n",
    "#avgGridDensity = np.average(gridDensity) #avgGridPop / (gridPL * gridPL) #these densities help home district algorithm\n",
    "maxGridDensity = np.max(gridDensity)\n",
    "print(\"done building grids. avgGridDensity, maxGridDensity,nPopulatedGrids =\",avgGridDensity,maxGridDensity,nPopulatedGrids)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "808f513f-21af-4b03-8061-fdeeed33dafd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here is a heat map of grid density \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(close=None, block=None)>"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"here is a heat map of grid density \")\n",
    "xyGridDensity = [[0.]*nGridsX for x in range (nGridsY) ]   #flipping x and y to get right orientation in pixel grid\n",
    "for nG in range (nGrids) : #  create polygon shape for each grid square, compute grid-square population density\n",
    "    x = int(nG % nGridsY)\n",
    "    y = int(nG / nGridsY)\n",
    "    if gridDensity[nG] > 0 :  #avoid log zero\n",
    "        xyGridDensity[x][y]=np.log(gridDensity[nG])\n",
    "c=plt.pcolormesh(xyGridDensity)\n",
    "plt.colorbar(c)\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "4013f80b-4fc8-40a1-b708-ee29c29b3ffe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.3623344010879333e-05 3.115583150001811e-05\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(np.min(vtdArea), np.min(tractArea))\n",
    "minTractPop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "ab450500-f1a4-4292-a2fe-71eb2217121d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I am working on tract number 0 of 1765 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 12 5.0 1 90.0 0.1972 33253.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 12 6.0 1 90.0 0.7129 33253.9\n",
      "we have 2 non-opposing shorted wedges for tract no 14\n",
      "I am working on tract number 20 of 1765 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 35\n",
      "we have 2 non-opposing shorted wedges for tract no 36\n",
      "we have 2 non-opposing shorted wedges for tract no 37\n",
      "7 yoyos for tract,wedge,wedgePop,r= 39 2 456562.50381310564 2.3898\n",
      "8 yoyos for tract,wedge,wedgePop,r= 39 2 21492.922328077664 1.1949\n",
      "9 yoyos for tract,wedge,wedgePop,r= 39 2 838167.426139659 2.6951\n",
      "10 yoyos for tract,wedge,wedgePop,r= 39 2 85185.24019752408 1.945\n",
      "10 yoyos for tract,wedge,wedgePop,r= 39 2 351917.14162331563 2.32\n",
      "11 yoyos for tract,wedge,wedgePop,r= 39 2 654512.1364596351 2.5075\n",
      "11 yoyos for tract,wedge,wedgePop,r= 39 2 491585.87299741444 2.4138\n",
      "12 yoyos for tract,wedge,wedgePop,r= 39 2 423404.07851800224 2.3669\n",
      "13 yoyos for tract,wedge,wedgePop,r= 39 2 457316.45400667854 2.3903\n",
      "13 yoyos for tract,wedge,wedgePop,r= 39 2 441274.39690850454 2.3786\n",
      "I am working on tract number 40 of 1765 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 41\n",
      "7 yoyos for tract,wedge,wedgePop,r= 45 2 957414.917128261 1.5876\n",
      "8 yoyos for tract,wedge,wedgePop,r= 45 2 42198.827086775564 0.7938\n",
      "9 yoyos for tract,wedge,wedgePop,r= 45 2 958326.2846386795 1.5967\n",
      "9 yoyos for tract,wedge,wedgePop,r= 45 2 684466.3884138116 1.1953\n",
      "10 yoyos for tract,wedge,wedgePop,r= 45 2 200636.23138134845 0.9945\n",
      "11 yoyos for tract,wedge,wedgePop,r= 45 2 434197.4932281135 1.0949\n",
      "11 yoyos for tract,wedge,wedgePop,r= 45 2 300578.67854070896 1.0447\n",
      "11 yoyos for tract,wedge,wedgePop,r= 45 2 241688.38706099635 1.0196\n",
      "12 yoyos for tract,wedge,wedgePop,r= 45 2 219411.97339904323 1.0071\n",
      "we have 2 non-opposing shorted wedges for tract no 50\n",
      "we have 2 non-opposing shorted wedges for tract no 52\n",
      "7 yoyos for tract,wedge,wedgePop,r= 53 3 32579.108552137623 1.299\n",
      "8 yoyos for tract,wedge,wedgePop,r= 53 3 1212815.079940488 4.418\n",
      "8 yoyos for tract,wedge,wedgePop,r= 53 3 1082002.8347885667 2.8585\n",
      "8 yoyos for tract,wedge,wedgePop,r= 53 3 668593.9690194506 2.0787\n",
      "9 yoyos for tract,wedge,wedgePop,r= 53 3 64359.55979655706 1.6889\n",
      "9 yoyos for tract,wedge,wedgePop,r= 53 3 166439.19487403674 1.8838\n",
      "9 yoyos for tract,wedge,wedgePop,r= 53 3 328649.468634623 1.9813\n",
      "9 yoyos for tract,wedge,wedgePop,r= 53 3 496192.2583283659 2.03\n",
      "10 yoyos for tract,wedge,wedgePop,r= 53 3 586715.1979712192 2.0544\n",
      "11 yoyos for tract,wedge,wedgePop,r= 53 3 545523.5611302147 2.0422\n",
      "11 yoyos for tract,wedge,wedgePop,r= 53 3 567117.4456639972 2.0483\n",
      "we have 2 non-opposing shorted wedges for tract no 54\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 57 5.0 3 90.0 3.8893 232008.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 57 6.0 3 90.0 3.4537 232008.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 58 5.0 2 90.0 3.8635 223273.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 58 6.0 2 90.0 3.5346 223273.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 58 13.0 2 90.0 4.6861 223273.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 58 14.0 2 90.0 4.2872 223273.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 58 15.0 2 90.0 3.9223 223273.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 58 16.0 2 90.0 3.5884 223273.7\n",
      "7 yoyos for tract,wedge,wedgePop,r= 58 2 223273.73185904504 5.3722\n",
      "loop31.0, tr58,wedgePops555193.23, 70179.3, 87512.0, 70163.0, 327339.0, Overedge?1, 0, 0, 0, ,Satisfied?0001,yoyo?0340 \n",
      "   targetWP, latest drx4 are tWP,dr, 327126.3,1.3505, 327126.3,-0.5254, 70179.3,0.0001, 327126.3,0.003\n",
      "loop32.0, tr58,wedgePops597726.01, 70179.3, 130044.7, 70163.0, 327339.0, Overedge?1, 0, 0, 0, ,Satisfied?0011,yoyo?0440 \n",
      "   targetWP, latest drx4 are tWP,dr, 327126.3,1.3505, 327126.3,0.1951, 70179.3,0.0001, 327126.3,0.003\n",
      "loop33.0, tr58,wedgePops1488123.91, 70179.3, 1020442.6, 70163.0, 327339.0, Overedge?1, 0, 0, 0, ,Satisfied?0011,yoyo?0440 \n",
      "   targetWP, latest drx4 are tWP,dr, 327126.3,1.3505, 327126.3,0.5059, 70179.3,0.0001, 327126.3,0.003\n",
      "loop34.0, tr58,wedgePops834007.22, 70179.3, 366326.0, 70163.0, 327339.0, Overedge?1, 0, 0, 0, ,Satisfied?0011,yoyo?0540 \n",
      "   targetWP, latest drx4 are tWP,dr, 327126.3,1.3505, 327126.3,-0.3002, 70179.3,0.0001, 327126.3,0.003\n",
      "loop35.0, tr58,wedgePops800779.62, 70179.3, 333098.4, 70163.0, 327339.0, Overedge?1, 0, 0, 0, ,Satisfied?0011,yoyo?0540 \n",
      "   targetWP, latest drx4 are tWP,dr, 327126.3,1.3505, 327126.3,-0.0162, 70179.3,0.0001, 327126.3,0.003\n",
      "loop36.0, tr58,wedgePops795853.9, 70179.3, 328172.6, 70163.0, 327339.0, Overedge?1, 0, 0, 0, ,Satisfied?0011,yoyo?0540 \n",
      "   targetWP, latest drx4 are tWP,dr, 327126.3,1.3505, 327126.3,-0.0024, 70179.3,0.0001, 327126.3,0.003\n",
      "I am working on tract number 60 of 1765 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 61 5.0 3 90.0 3.9286 232618.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 61 10.0 3 90.0 4.504 232618.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 61 11.0 3 90.0 3.9913 232618.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 61 16.0 3 90.0 4.8149 232618.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 61 17.0 3 90.0 4.2669 232618.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 61 18.0 3 90.0 3.7812 232618.5\n",
      "7 yoyos for tract,wedge,wedgePop,r= 61 3 232610.56455918052 3.6078\n",
      "8 yoyos for tract,wedge,wedgePop,r= 61 3 25368.685487039125 1.8039\n",
      "9 yoyos for tract,wedge,wedgePop,r= 61 3 232618.52695179541 4.3471\n",
      "loop31.0, tr61,wedgePops1603289.51, 29636.5, 1175859.9, 29721.5, 368071.7, Overedge?1, 0, 0, 0, ,Satisfied?0001,yoyo?0211 \n",
      "   targetWP, latest drx4 are tWP,dr, 367669.1,1.7676, 367669.1,0.9557, 29636.5,-0.0043, 367669.1,-0.0005\n",
      "loop32.0, tr61,wedgePops1180948.77, 29636.5, 753586.2, 29654.4, 368071.7, Overedge?1, 0, 0, 0, ,Satisfied?0001,yoyo?0311 \n",
      "   targetWP, latest drx4 are tWP,dr, 367669.1,1.7676, 367669.1,-0.5294, 29636.5,-0.0006, 367669.1,-0.0005\n",
      "loop33.0, tr61,wedgePops514009.29, 29636.5, 86646.7, 29654.4, 368071.7, Overedge?1, 0, 0, 0, ,Satisfied?0011,yoyo?0311 \n",
      "   targetWP, latest drx4 are tWP,dr, 367669.1,1.7676, 367669.1,-0.625, 29636.5,-0.0006, 367669.1,-0.0005\n",
      "loop34.0, tr61,wedgePops557070.11, 29636.5, 129707.6, 29654.4, 368071.7, Overedge?1, 0, 0, 0, ,Satisfied?0011,yoyo?0411 \n",
      "   targetWP, latest drx4 are tWP,dr, 367669.1,1.7676, 367669.1,0.2448, 29636.5,-0.0006, 367669.1,-0.0005\n",
      "loop35.0, tr61,wedgePops1554955.97, 29636.5, 1127593.4, 29654.4, 368071.7, Overedge?1, 0, 0, 0, ,Satisfied?0011,yoyo?0411 \n",
      "   targetWP, latest drx4 are tWP,dr, 367669.1,1.7676, 367669.1,0.686, 29636.5,-0.0006, 367669.1,-0.0005\n",
      "loop36.0, tr61,wedgePops979507.2, 29636.5, 552144.6, 29654.4, 368071.7, Overedge?1, 0, 0, 0, ,Satisfied?0011,yoyo?0511 \n",
      "   targetWP, latest drx4 are tWP,dr, 367669.1,1.7676, 367669.1,-0.3907, 29636.5,-0.0006, 367669.1,-0.0005\n",
      "loop37.0, tr61,wedgePops717163.68, 29636.5, 289801.1, 29654.4, 368071.7, Overedge?1, 0, 0, 0, ,Satisfied?0011,yoyo?0511 \n",
      "   targetWP, latest drx4 are tWP,dr, 367669.1,1.7676, 367669.1,-0.1212, 29636.5,-0.0006, 367669.1,-0.0005\n",
      "loop38.0, tr61,wedgePops767706.19, 29636.5, 340343.6, 29654.4, 368071.7, Overedge?1, 0, 0, 0, ,Satisfied?0011,yoyo?0611 \n",
      "   targetWP, latest drx4 are tWP,dr, 367669.1,1.7676, 367669.1,0.0297, 29636.5,-0.0006, 367669.1,-0.0005\n",
      "loop39.0, tr61,wedgePops794029.15, 29636.5, 366666.6, 29654.4, 368071.7, Overedge?1, 0, 0, 0, ,Satisfied?0011,yoyo?0611 \n",
      "   targetWP, latest drx4 are tWP,dr, 367669.1,1.7676, 367669.1,0.0126, 29636.5,-0.0006, 367669.1,-0.0005\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 0 2.41748 15999.4568 29636.4554 1\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 1 1.2926 340343.6367 366666.5978 0\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 2 0.2467 29721.479 29654.4202 0\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 3 0.71211 369520.6327 368071.6802 0\n"
     ]
    },
    {
     "data": {
      "image/png": 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ggDBCAsII9A2mVMnSRv4IooDt2LOfpctXA9Yprt3cisbFFCkIosjYtXcbc6MimbfkV44eP4pSX9Cq6VmeHb+Arp164Onxv7tede9svenJwSP7MK2KxZQYzew/f+Hn2VPx9PCkY9vOhASEERwYRrOGreTsoZD54sdfAShbuhT9QoONDeNASmttdIZr+Pr66uTkZKNjiELgn5NHWRD9G3OXRLJt9ybc3d3p1qknAyOGEx7Uj5Il8j59cVZ2Fms3JWJKjCYuKYYdf28BoErFatZLS4FhdPXrQbmyMpbBlR04cozOdz8AwDsvPM59g/sYGyiPlFLrtNa+d/QeUhBEYXMh9TxLls1nzpKZJCab0FrTrkVHBkQMp1/oECpVyJ/upf+cPErCqqXEJUWTsGop5y+m4ObmRruWfoQEhBES2IvWzdoXmcsNhcXzUz7ll7l/ArB35SK8vYoZnChvHFIQlFLeQALghfUS02yt9WSl1AdAXyAL2AuM1lqn5LL9AeAiYAZy8hJYCoK4VVnZWcStjGJuVCSxCYvIzMqkbs0GDIwYzt29htGgTuMC3X9OTg4btydjSorGlBjDxu3JaK0p71ORIP+eBAeEEeQfmm/FSBSM46fO0CHCOhr51UljGe9Cs5o6qiAooKTWOlUp5QmsACYBZYBlWuscpdR7AFrr53PZ/gDgq7U+nddQUhBEXlgsFpI3JzFn8UwW/TWHlPNnKe9Tkf5h9zAgYjjtW/oZdm3/zLlTJKxeSlxiDPGrYjl99iQArZq2szZOB4bTvlWnK9othPHe+GQaU3+ZDcCu+PmUKuk606A7/JKRUqoE1oLwiNZ69WXPDwAGa62vGconBUHkt937tjN3SSTzon7lyD8H8fYqTkRIfwb0Gk43/55O90fWYrGwbfcm4hKjMSXFkLw5CbPZTOmSZejq153gwHCCA8KoUbWW0VGLtHPnL9Cyx2AAnhw7kmcevs/gRLfGYQVBKeUOrAMaAl9efSaglPoD+E1r/Usu2+4HzgEamKq1nnadfYwDxgHUrl27w8GDB2/xRxGF2fFTx1gQPYu5S2aydddG3Nzc7I3DvYL731LjsNEupJ5nxZplmJJiWJYYzT8njgDQuH4zggPCCQkMx69tZ7y9vA1OWrR8NG06H06bDsCWpbMp7+Na06AbcYbgA8wDHtNab7U99zLgCwzUubyZUqq61vqYUqoyEGvbNuFG+5EzBAFwMfUCi+PmM29JJCvWLkNrTdvmvvbG4coVXf8mJVpr/t6/g7hE67iH1RuWk5WdRXHvEgT6Btu7ttarJRPvFaS0S+k07tYfsN4r+fWnxhuc6NblR0G4pXEIWusUpZQJ6AVsVUrdD/QBeuRWDGzbHLP9f1IpNQ/ww9pILcQ1srKzMCXFMHfJTGITFpGRmUGdGvWZ9NCLDOg1nIZ1C9dsk0opGtdvTuP6zXl45BNcSk8jcV08psQY4pJi+GvFYgDq1mxg79ra2TeYEsVLGpy8cPm3VxHAwyMHG5jEWHlpVK4EZNuKQXEgBngPyAE+AoK01qeus21JwE1rfdG2HAu8obWOutE+5QyhaNFak7wpiblRkSyM/Z2U82cpV7aCvXG4Q6tORXbg1/7De4hPiiUuKZqVa02kZ1yimGcx/Np1sZ49BITRpEGLInt88kNmVhYNu/TDYrEw4u4IPnjlSaMj3RZH9TJqDfwEuGOdDG+W1voNpdQerF1Rz9hWXaW1Hq+Uqg58p7XurZSqj/USE1jPRmZqrd++WSgpCEXDngM7mbN4JvOjf+PQ0f14exUnPKgvA3uPIMg/1Okah42WmZXJmo0rMSXFYEqMZufebQBUq1LTXhy6+HWnbGkfY4O6mBnzFvPc258AsHzuj9SvXcPYQLdJBqYJl3Pi9D/2xuEtOzfg5uZGV78eDOg1jIiQu2WOoFtw7MQR+5xLy1f/xcW0C7i7u9Ohlb+9a2vLJm1lYNwN5OSYaRN+DynnL9IvNIiv33nZ6Ei3TQqCcAmpaRdZEjefubbGYYvFQutm7RkYMYJ+YUOoUrGa0RFdXnZONhu2rrF3bd28Yz0AFcpVIsg/lJBA68C4CuUqGZzUucyPimPCK+8AEDPza1o0bmBwotsnBUE4reycbExJMcxbEkl0/B9kZKZTu0Y9BvQaxsCI4TSs29ToiIXa6bMniV8Va728lBTL2ZTTKKVo3ay9bdbWcNq19MPDo+jOb6m1psuA0Rw4cozugR2Z/tlNr2Y7NSkIwqlorVm3ZTVzl8zkj9jZnE05TbmyFegbOpiBESPwbe0vjZ8GsFgsbNm5wX72sG7LKiwWC2VL+9C1Uw9CAsIJCgilWmXXvHZ+u2KXr+KBJ18DYP53H9OxbQuDE90ZKQjCKew5sIt5tmmlDx7dh7eXN2Hd+jIgYjjBAWEU83SNycGKipQL5+wD4+KSYjh+8igAzRq2tHVtDadjm0C8inkZnLTgaK3pfd9ENu/4mw6tm7Pwh0+MjnTHpCAIw5w8fZwFMbOYFxXJpu3rcHNzo7NvCAN7Dyci+G5Kl3KtUZ5FldaanXu32sc9rNmwguycbEoUL0ln32CCA8MJCQijTs36RkfNV0nrNjP44WcAmPH5FIID7ujvqFOQgiAcKu1Sqr1xePmav7BYLLRq2o4BvYbTP/weqlaqbnREcYfSLqWyMtlk77106Oh+AOrVbkiIbVqNgA7dKO7tOpO+5Wboo8+zYs0GGtWrTdysbwvFpUwpCKLAZedkk7BqKXOXzCQ6/g/SMy5Rq3pde+Nwo3rNjI4oCojWmv2H99jPHhKTTWRkpuNVzAv/9t1sXVvDaFi3qUv9Qd28YzcRoyYCMPXdV+jTs5vBifKHFARRILTWrN+6hnlLIlkQM4uzKafxKVuevj0HMyhiBL5tAlzqD4DIHxmZGazZsIK4pBjiEqP4e/9OAGpUrU1wQBjj7n3cJXqPjX32DRbHraBKxfKs/XMG7u7uRkfKF1IQRL7ae3C3vXH4wJG9eHt507PrXQyKGEFwYLg0DosrHPnnIKakWExJ0cSvWkrlitVY9tsGp26M3nPgEEGDxwDw0WtPM7RfuMGJ8o/DJ7cThc+pMydYGPs7cxfPZOP2ZJRSdO4YwuMPvkBE97spU6qs0RGFk6pZrQ4jB45h5MAxxK+KZcTEu/hm+kdMeuhFo6Nd15c/zQLA26sYAyK6G5zG+UhBKILSLqUSZVrAvKhfSVi9FLPZTIvGbXj1iffoH3ZPkeuPLu5ckH8od/UYyGc/vMvAiOHUql7X6EjXOHr8JLP+iAHg5cfGUMxT5sq6mhSEIiInJ4eE1dbG4SjTQtIzLlGzWh0eue9pBvYaTpMGrj0oRxhv8pMfEJcYzeQPn+aHD+cYHeca30z/3b48/O5eBiZxXlIQCjGtNRu2rbU3Dp85dwqfMuUY1PteBkYMp2ObQJn4TOSbGlVr8eSYl3n785dYumIxPbv0NjqS3emz5/jhtwUAPP/oaIp7y93ociMFoRDad+hv5i2JZG7Urxw4vAevYl5XNA47c6OfcG1jRjzOrEU/8+oHT9LZN4Ti3sWNjgTAt5Hz7Mv3D+lrYBLnJgWhkDh99iQLY35n7pKZbNi2FqUUgb7BPPbAc/TuMUAah4VDFPMsxlvPfcrQR8L56qcPePrh14yOxIXUNL748VcAJj4wlLKlXef+244mBcGFXUpPIzp+IXMXRxK/Ohaz2Uzzxq15ddK79Au7h+pVahodURRBXTqG0D/sHr786QMG3XUvdWsaO6X0T78vtC+PGT7QwCTOTwqCi8nJyWH5mr+YuySSKNMCLqWnUaNqbR4Z9RQDeg2nacOWRkcUgteefJ+lKxbz6gdP8fMn8w0byJiekcG7X/4IwAND+lGpQjlDcrgKKQguQGvNpu3rmLtkJgtiZnH67EnKlvZhQK/hDIwYjl/bztI4LJxK1UrVeXrca7zxyXPExP9BeHA/Q3L8uiDavvzIfUMMyeBKZKSyEztwZC/zlkQyZ8lM9h/aQzHPYvTsehcDI4bTvXOENA4Lp5adk034iI6kpadh+n2TwyfEy87JoVnwANIzMhnSJ5RPXn/Woft3NBmpXAidOXfK2jgcFcn6LatRSuHfvhsT7nuW3j0GyA3Uhcvw9PBkygufM2hcDz774V2ef/QNh+5/XtQy0jMyAZhw/1CH7ttVSUFwAukZl4g2LWTOkkjiV8VgNptp1qgVLz82hf7hQ6lRtZbREYW4Lf7tuzIwYgTfTP+IwXeNpEGdxg7Zr9ls5j1b20FESGca1avtkP26OikIBsnJyWFlchxzFs9kSdx8LqWnUa1KTcaPfJIBEcNp1rCV0RGFyBevTnqX2IRFvPrBk8z4fJFDGpijTIkcP3UGgMdGDy/w/RUWUhAcSGvN5h3rmbskkgUxv3HqzAnKlCrL3eFDGRgxgk7tukjjsCh0KlesyrOPvM5r/3mKxcvmcVePgu36qbXmw6k/A9C5Y1vaNHfMWUlhIAXBAQ4e2cfcqEjmLYlk78HdFPMsRo8uEQyMGEH3zhF4e8kwelG43T94PL8u/C+TP3qG4IAwSpYouMFhCavXsWvfQQCeeOjeAttPYSQFoYCcTTnNwtjZzF0yk3WbVwEQ0L4bD498krt6DMSnjPSHFkWHh4cHU57/nLsfCuLT76fw0mNTCmxfH387A4BWTRsS0KF1ge2nMJKCkI/SMy4RE/8Hc6MiMSXGkGPOoWmDFrw08W3u7jWUGlWlYUsUXR3bBDC07/1M/eUThvQZVSC3X127aRtrN20D4Klxo+TOfrfopgVBKeUNJABetvVna60nK6U+APoCWcBeYLTWOiWX7XsBnwLuwHda63fzL77xzGYzK9fGMWeJtXE47VIqVSvXYOyIxxnYewTNG8k3FCH+9dJjbxNlWsDL7z/Bb19F5fsf7M9tcxbVqVGNnl065et7FwV5OUPIBLprrVOVUp7ACqXUEiAWeFFrnaOUeg94EXj+8g2VUu7Al0AocARYq5RaqLXenq8/hYNprdm6ayNzFs9kYcwsTpz+h9Ily9AvdAgDIobj365roblPqxD5qWL5yjz36Bu8/N7jLIz9nf5h9+Tbe2//ex9/rVgNwLOP3C8dNG7DTQuCtg5lTrU99LT901rrmMtWWwUMzmVzP2CP1nofgFLqV6A/4JIF4dDR/cyL+pW5S2ay58AuPD087Y3DPbr0lsZhIfJg1MCx/LrgR974+Dl6dI6gVMnS+fK+/85o6lOmNH17BuXLexY1eWpDsH3TXwc0BL7UWq++apUHgd9y2bQGcPiyx0eAXM/jlFLjgHEAtWs7z7X2syln+GPpbOYtiWTtpkTAOthm7IhJ3NVjIOXKljc4oRCuxd3dnSkvfE6/0V356Nu3eO2J9+74PfcfPsqCGBMAL0wYjYeHnKHfjjwVBK21GWirlPIB5imlWmqttwIopV4GcoAZuWya2wXCXCdP0lpPA6aBdS6jvOQqKOkZ6cQuX8TcxTOJS4wmx5xDk/rNeXHiW9wdPpSa1eoYGU8Il9e+pR8j7n6Q7yI/454+o+54lt6vf7beHlMpxZA+YfkRsUi6pV5GWusUpZQJ6AVsVUrdD/QBeujcZ8k7Alw+70JN4NhtZi1QZrOZlckm5kVFsnjZPFLTLlK1UnXGjHicgRHDad6otfRYECIfvTDhTf5cNo+X35/E7KlLb/v36/ipM8yYtxiAVyaNxdurWH7GLFLy0suoEpBtKwbFgZ7Ae7beQ88DQVrrS9fZfC3QSClVDzgKDANG5E/0O6e1ZtuujcxZEsmC6N/sjcN9egxiQMRwAtp3k8ZhIQpIeZ+KvDTxbZ57+xHmLpnJoN63N4hs2ow59uWRA5znPs6uKC9nCNWAn2ztCG7ALK31IqXUHqxdUWNtlX2V1nq8Uqo61u6lvW09kCYC0Vi7nf6gtd5WMD9K3h0+dsDeOPz3/p14enjSvXMve+Ows9wHVojCbnj/0UTO/4E3P32B0G59bvlWr2dTLjD1l9kAPDV2JKVKOnaK7cKmyNwP4WzKGRYtncO8qEjWbFwJgF/bzgyMGEGfnoOkcVgIg2zesZ7e9wXw4NAJvPHMR7e07YdTf+ajb38BYMvS2ZT3KVMQEV2C3A/hJtIz0lm64k9743B2TjaN6jXl+UffYECvYdSqXtfoiEIUea2btWfUoHH8OOsrhva7nxaN2+Rpu9S0S/ZiMO7eQUW6GOSXQlcQzGYzievimbfE2jh8Me0CVSpW48FhExnYaxgtmrSVxmEhnMzzj77BoqVzeOndx5n3XVyeBpX9YmtIBmtBEHeuUBWEb375mG9nfMrxU8coVbI0vbsPYGDECAI7BEnjsBBOzKdMOV55/B2eemMsv//5C0P73nfD9TOzsnjr028BuHdABNUqV3REzEKvUI3tTkyO5/gpa6/WMqV88PYqTnp6GhmZ6QYnE0LczJA+o+jQ2p+3P3uRlAvnbrju74ti+bf985H78m/6i6Ku0DUqHzq6H1NSLKakaFasjSPtUiqeHp50bNuZkIAwggPDaNawlVw2EsIJbd21kYhR/owaNI4pz3+W6zo5OWbahN1DyoWL9A8L5qspLzk4pXPKj0blQlcQLpeVncXaTYmYEqOJS4phx99bAKhaqTrBtuLQ1a+H3JtACCfy6gdP8uOsr1j8cxKtm7W/5vX5UXFMeOUdAGIjv6F5o/qOjuiUpCDcouOnjhGfFEtcUjQJq5Zy/mIKbm5utG/ZieDAMEICwmndrL3MkiiEgc5fTCFocCtqVqvDwh8Srvh9tFgsdBkwmoNH/6F7Zz+mf/qWgUmdixSEO5CTk8PG7cmYkqIxJcawcXsyWmvK+1QkyL8nwQFhBPmHUqlClQLNIYS41uw/f2HS5Af54JVvGHH3g/bnYxKSGP3UZAAW/PAJvq2bGxXR6UhByEdnU06TsGopcUkxmJJiOH32JACtmrYjOCCMkMBwOrTyx8OjUHXMEsIpaa0ZNK4Hu/ftIGHOVsr7VEBrTcSoCWzZuYeObVow//uPjY7pVKQgFBCLxcK23ZuIS4zGlBRD8uYkzGYzZUqVpYtfd0ICwwnyD6VG1Vo3fzMhxG3ZsWcL4ff6MazfaN5/+SsSkzcxZPyzAMz8YgpB/nf0t6/QkYLgIBdSz7NizTJMSTHEJcZw7IT1Fg9N6jcnODCc4IAwOrXrglcxL4OTClG4/N/Hz/HtzE/548cVvPvVbFas2UDj+nVY9ts06Sl4FSkIBtBa8/f+HcQlWi8trVqfQFZ2FsW9SxDoG2zv2lqvVkOjowrh8i6mXiBocCtKl6rBngOlAJj23qvc1aOrwcmcjxQEJ3ApPY2kdQnE2bq2Hji8B4C6NRtY2x46hxPYIYgSxUsanFQI1/T7ohk88fpPAFStVIG1f86QnoC5kILghPYf3mPv2rpyrYn0jEsU8yxGp3ZdbV1bw2hcv7mc7gqRR1nZ2dQLuAuAN58Zx4PDcrt9u5CC4OQyszJZs3ElpqQYTInR7NxrvRVEtSo1rZeWAsLo4tedsqV9jA0qhJNbvmYlIyaMZvBdQXz8+rdGx3FKUhBczLETR+xnD8tX/8WF1PO4u7vToZW/vWtryyZt5XRYiFy8/dmLfPXzh8z/Pp6ObQKMjuN0pCC4sJycHDZsXUNckrVr66bt6wCoUK4SQf6hhARaB8ZVKFfJ4KRCOIe0S6kEDWlN+bIVWPxzkowJuooUhELk9NmTxK+KtV5eSorlbMpplFK0adaB4MAwggPCadeio/wSiCJt0dI5PPzCcN585mMeHDbB6DhORQpCIWWxWNiyc4N9YNy6LauwWCyULe1D1049CAkIJygglGqVaxgdVQiH0lozYuJdbNi6huVzt8nUMpeRglBEpFw497+BcUkxHD95FIBmDVvaZm0Np2ObQBkYJ4qEvQd302NoO/qF3cNnb/xodBynIQWhCNJas2vvNkxJMSxLjGbNhhVk52RTonhJOvsGExwYTkhAGHVqypTAovB698tX+fzH95gz7S/828sgNZCCILA2tK1MNtmm1Yjm0NH9ANSv3YgQ27QaAR26Udy7hMFJhcg/6RmXCBrcmtIlSxM1Yw2eHp5GRzKcFARxBa01+w/vwZRovbSUmGwiIzMdr2Je+LfvZuvaGkbDuk1lYJxweVGmBTz0zBAmP/kB4+6dZHQcw0lBEDeUkZnBmg0rbFN6R7N73w4AalStbS8OXTp2p3SpMgYnFeLWaa25b1J/1mxaSfzsLVStVN3oSIaSgiBuydHjhzAlxRKXGMXyNctITbuIh7sHvm0C7APjWjRuI2cPwmUcOLKX7ve0JSLkbr58e7rRcQwlBUHctuycbNZtXmUdGJcYw9ZdGwGoXKEqQQGhhASE0bVTT8r7VDA2qBA38Z+p/8fH377NrG9i6OwbbHQcwzikICilvIEEwAvwAGZrrScrpYYArwPNAD+tda5/wZVSB4CLgBnIyUtgKQiOd/L0cfvAuPhVSzl3/gxKKdq26Gib0jucts19cXd3NzqqEFdIz0in+9C2eBXzImZmMsU8ixkdyRCOKggKKKm1TlVKeQIrgEnAecACTAWeuUlB8NVan85rKCkIxjKbzWzesR5TknVK7w1b12CxWPApU45unXrabgoUSpWK1YyOKgQAscv/5IEnB/DK4+/wyH1PGx3HEA6/ZKSUKoG1IDyitV5te86EFIRC7dz5sySsXmqbtTWGk2eOA9C8cWvbrK3h+LYJKLLfzIRzGP3UQFasjSN+9haqV6lpdByHc1hBUEq5A+uAhsCXWuvnL3vNxI0Lwn7gHKCBqVrraddZbxwwDqB27dodDh48eGs/iXAIrTXb/95s79q6duNKcsw5lCpZmi4dQwgOsJ491Kpe1+iooog5dHQ/Ife0oWfXu5j6bqTRcRzOiDMEH2Ae8JjWeqvtORM3LgjVtdbHlFKVgVjbtgk32o+cIbiO1LSLrFwbR5xtYNyRf6yFvGHdJgQHhBMSGEandl0p7l3c4KSiKPjkuyl88M3rRH6xmG7+PY2O41CG9DJSSk0G0rTW/7E9NnGDgnDVtq8Dqf9uez1SEFyT1pq9B3fbZmyNIWldPBmZGXh7eRPQIcg671JAGA3qNJauraJAZGRm0GNYe9yUYumv64vU/F6OalSuBGRrrVOUUsWBGOA9rfUi2+smrlMQlFIlATet9UXbcizwhtY66kb7lIJQOKRnpLNqfYJ1YFxiNHsP7gagVvW61nEPAWF07hhCqZKlDU4qCpO4xGhGPt6XFya8yWOjn7/5BoWEowpCa+AnwB1wA2Zprd9QSg0APgcqASnARq11uFKqOvCd1rq3Uqo+1ktMYO2yOlNr/fbNQklBKJwOHd2PKSkWU1I0K9bGkXYpFU8PTzq27Wzr2hpGs4at5OxB3LGxz97DssRo4mdvpma1OkbHcQgZmCZcVlZ2FsmbkuxdW7fv3gxAlYrVbFN6h9HVrwflypY3OKlwRUePHyJocGuCA0L57oPfjY7jEFIQRKFx/NQx4lctxZQYTcLqpaRcOIebmxvtWvrZu7a2btZeBsaJPPviv+/zzhevMP3ThXTv3MvoOAVOCoIolMxmMxu3JxOXGIUpMYaN25PRWlOubAWC/HsSEhhOkH+o3C1L3FBWdhY9h7XHbDbz128b8PbyNjpSgZKCIIqEsymnSVi11DZrawynz54EoFXTdvZJ+dq36iRz4otrJKz+i+ETInjm4dd4cuwrRscpUFIQRJFjsVjYvnuTvTis3ZSI2WymdMkydPXrbp9Wo0bV2kZHFU5i/IsjiE1YxLLfNhbqOwlKQRBF3oXU89aBcYnWgXHHThwGoHH9ZtaBcQFh+LXrUugvF4jr++fkUboNakln32D++/G8m2/goqQgCHEZrTV/799BXKL17GHV+gSysrPw9ipOoG+wvWtr/dqNjI4qHOyb6R/x5qcv8ONHcwnr1sfoOAVCCoIQN3ApPY2kdQnW+00nRbP/0B4A6tZsYO/aGtghiJIlShmcVBS07Jxswkb4kp6RTtysTYVyKhUpCELcggNH9loHxiVGszLZxKX0NIp5FsOvXRdb19YwmjRoIQPjCqnE5HiGjA/liTEv8ez4142Ok++kIAhxmzKzMlm7KRFTYjSmpBh27NkKQNXKNezFoWunHpQt7WNsUJGvJr5yH3/+NZdlszZSr1ZDo+PkKykIQuSTYyeOEJ8US1xSNMtX/8WF1PO4u7vTvmUne9fWVk3b4ebmZnRUcQdOnP6HboNa0rFNINM/XViozgalIAhRAHJyctiwdY31ftNJMWzavg6ACuUqEeQfSkhgGEH+oVQoV8ngpOJ2fDvzM17/6Bm++2AWESF3Gx0n30hBEMIBTp89ScLqpcQlxhC/KpYz506hlKJ1s/a2WVvDadfSDw8PD6OjijzIycmh10g/zl88T/zszZQoXtLoSPlCCoIQDmaxWNi6ayNxidHEJUazbssqLBYLZUqVpWunHoQEhBMUEFokb+HoStZsXMmAMSFMHP0cL054y+g4+UIKghAGS7lwjhVrltm6tsZw/ORRAJo2aEFwYDghgeF0bBNYpG7U4iqeeP0h5kf9ytJf19OwbhOj49wxKQhCOBGtNbv2brMXhzUbVpCVnUWJ4iXp7BtsLRABYYV6+gRXcurMCboNakmb5r5EfrnY5RuYpSAI4cTSLqWSuC4ek21ajYNH9wFQr3ZDQgLCCQ4II9A3iOLeJQxOWnT9d9bXvPz+JL55dyZ9ew42Os4dkYIghAvZf3gPcSutNwRKTDaRkZmOVzEvOrXrauvaGkajes1c/puqKzGbzfS+L4DT504R//tml76dqxQEIVxURmYGazassM3aGs3ufTsAqF6lFiGB1oFxXfy6U6ZUWYOTFn7rtqym3+iuPDLqKV6Z9K7RcW6bFAQhComjxw/Zp9VYvmYZF9Mu4OHugW+bAFvX1jCaN24jA+MKyDNvPszvi6YTM3MtTRq0MDrObZGCIEQhlJ2Tzfotq4mzTauxZecGACpVqGIdGBcQRjf/npT3qWhw0sLjzLlTdBvUkmaNWvP7NzEuedlOCoIQRcCpMyeIXxWLKSkGU1Is586fQSlF2+a+BAda7zfdrkVHud/0HZo+51teeGcCX7z1EwN6DTc6zi2TgiBEEWM2m9m8Yz2mJGvj9Iata7BYLPiUKWcfGBccGEaVitWMjupyzGYzfUd34Z8TR0mYs5XSpcoYHemWSEEQoog7d/4sy9f8hcl2U6ATp/8BoHnj1rZZW8PxbRNAMc9iBid1DRu3JdPngc6MGf44rz/1gdFxbokUBCGEndaaHXu2WMc9JMWwduNKsnOyKVmiFF06hlhvKRoYRq3qdY2O6tSenzKByAU/ED1jDc0atjI6Tp5JQRBCXFdq2kUSk00sszVOHz52AIAGdRrbR037t+9WKO8edifOnT9L14EtaFSvKXO/XeYyDcwOKQhKKW8gAfACPIDZWuvJSqkhwOtAM8BPa53rX3ClVC/gU8Ad+E5rfdOOvlIQhMhfWmv2Htxta5iOIWldPBmZGXh7eePfvpv9ng8N6jR2mT+ABSly/o8889bDfPL69wzpM8roOHniqIKggJJa61SllCewApgEnAcswFTgmdwKglLKHdgNhAJHgLXAcK319hvtUwqCEAUrPSOd1RuWE5doHRi358AuAGpVr2sf99C5Y4hLj9y9ExaLhf4PBXHo6H4S5mx1iTvnOfySkVKqBNaC8IjWerXtORPXLwgBwOta63Db4xcBtNbv3Gg/UhCEcKzDxw5YB8YlWQfGpV1KxcPdg45tO1sbpwPDaN6odZE6e9i6cwMR9wVw/+DxvPXcJ0bHuSmHFQTbN/11QEPgS63185e9ZuL6BWEw0EtrPcb2eBTQSWs98Ub7k4IghHGysrNYt3kVcYlRxCXFsH33ZgCqVKxGUEAowQFhdOvUk3JlyxuctOC98v4T/DT7G5b8nETLpu2MjnNDRpwh+ADzgMe01lttz5m4fkEYAoRfVRD8tNaP5bLuOGAcQO3atTscPHjwln8YIUT+O37qGPGrlmJKjCZh9VJSLpzDzc2Nti062s4ewmnTrEOhHBh3/mIKXQe2oE7N+iz4Pt6ppw4xpJeRUmoykKa1/o/tsQm5ZCREkWA2m9m4PRlTonVg3MZta9FaU65sBYL8exIcYL3fdOWKVY2Omm9mLfqZJ18fw4evTmNY/weMjnNdjmpUrgRka61TlFLFgRjgPa31ItvrJq5fEDywNir3AI5ibVQeobXedqN9SkEQwjWcTTnD8tVLbbO2xnDqzAkAWjZpS3Cg9X7THVr74+nhaXDS26e1ZuDY7uw5sIuEOVud9lKZowpCa+AnrN1G3YBZWus3lFIDgM+BSkAKsFFrHa6Uqo61e2lv2/a9gU9s2/+gtX77ZqGkIAjheiwWC9t3b7IXh+RNSeSYcyhVsjRd/brbB8bVqFrb6Ki3bPvfm+k1shMj7n6Id1/8wug4uZKBaUIIp3Uh9Twr18bZurbGcPT4IQAa1WtqLw6d2nXF28vb4KR5M/nDZ/j+18/586dE2jTvYHSca0hBEEK4BK01ew7stBeHVesTyMzKxNurOIG+wfaurfVqNXTarq0XUs8TNKgV1avUZOGPy52uEV0KghDCJaVnXCJpXQKmpBiWJUax/9AeAOrUqG+b0juMzr7BlCxRyuCkV5oXFcnEV+7nvZe+YuTAMUbHuYIUBCFEoXDgyF77HeNWJpu4lJ6Gp4cnfu262M8emjZoafjZg9aaIePD2LFnC8vnbHWqmxRJQRBCFDqZWZms3ZSIyTYp3449WwGoWqm6fc6lrp16GDadxK692wgb0ZF7+t7HB698Y0iG3EhBEEIUev+cPEp8UixxSdEsX/0X5y+m4O7uTvuWnewFolXTdg4dNPbmpy/wzfSPWPjjcjq06uSw/d6IFAQhRJGSk5PDhm1riUuMwpQUw6bt6wAo71ORYNu0GkH+oVQsX7lAc6SmXSRocCsqlq/M4p+TnKKBWQqCEKJIO332JAmrlxKXGEP8qljOnDsFQOtm7e1nD+1bdsLDwyPf970w9nceefFe3n7+Mx4YMj7f3/9WSUEQQggbi8XC1l0bibO1Pazbsgqz2UyZUmXt95sOCgilepWa+bI/rTXDJ/Rm8451JMzZWuBnJTcjBUEIIa7j/MUUVqxZZuvaGs3xk0cBaNqgBcGB4QQHhOHXtjNexbxuex97Duyk57AODIgYzseTv8uv6LdFCoIQQuSB1prd+7Zbp9VIjGH1huVkZWdR3LsEnTsGExIQTnBgGHVrNrjl937ni5f54r8fMP87Ex3bBhZA+ryRgiCEELfhUnoaK5NNmBJjiEuM5uDRfQDUrdXQPu4hsEMQJYqXzNN7BQ1uTdkyPkRNX10g7RV5IQVBCCHywf7De6zFISmGlWvjyMhMx6uYF53adbU1TofRqF6z6w6MW7xsHmOfG8r/Pf0hY4Zfc7sXh5CCIIQQ+SwjM4M1G1diSorBlBjNrn3WW8BXr1KLENu0Gl38ulOmVFn7NlprRk3qR/KmJOLnbKFKxWoOzy0FQQghCtjR44eJXxVLXKJ1YNzFtAu4u7vj2zrA3rW1ReM2HDiylx5D29Gn5yA+f/Mnh+eUgiCEEA6UnZPN+i2r7V1bt+zcAEDF8pUJ8g9l38HdbNi2ltlTlxLQoZtDs0lBEEIIA506c4L4VbHWy0tJsZw7fwawDoxbMn2VQ7PkR0EwpjlcCCEKgUoVqjD4rpEMvmskZrOZLTs3EJcUjZty3LxK+UkKghBC5AN3d3fatvClbYs7+pJuKNcsY0IIIfKdFAQhhBCAFAQhhBA2UhCEEEIAUhCEEELYSEEQQggBSEEQQghhIwVBCCEE4KRTVyilTgEHb7JaReC0A+LkN1fMLZkdwxUzg2vmLoyZ62itK93JDpyyIOSFUir5TuftMIIr5pbMjuGKmcE1c0vm3MklIyGEEIAUBCGEEDauXBCmGR3gNrlibsnsGK6YGVwzt2TOhcu2IQghhMhfrnyGIIQQIh9JQRBCCAE4YUFQSg1RSm1TSlmUUr6XPV9BKRWnlEpVSn1xnW0XKqW23uC9X1RK7VFK7VJKhRuZWSkVpZTaZNvuG6WUey7vW1cpla6U2mj7942zZ7atVyDH+XZyK6VKKKX+VErttG337nXe12mOdV4z29Z1ps/020qpw0qp1Bu8r9Mc57xmtq3nNJ9p22sdlFJbbJk+U0qpXN731o+11tqp/gHNgCaACfC97PmSQBdgPPBFLtsNBGYCW6/zvs2BTYAXUA/YC7gblRkoY/tfAXOAYbm8b93r/TxOnLnAjvPt5AZKACG25WLAciDCmY/1LWR2ts+0P1ANSL3B+zrNcb6FzE71mba9tgYIsP0uLsmvz7TTnSForXdorXfl8nya1noFkHH1a0qpUsBTwFs3eOv+wK9a60yt9X5gD+BnVGat9QXbogfWX3qHtu4XYOYCO863k1trfUlrHWdbzgLWAzXzK09eFGBmZ/tMr9Ja/5Mf+78dBZjZqT7TSqlqWL+cJWnrX/6fgbvzI4vTFYTb9CbwIXDpBuvUAA5f9viI7TnDKKWigZPARWD2dVarp5TaoJSKV0p1dVy63OUhs9Md538ppXyAvsBf11nFqY413DSz0x7rm3C643wTznaca9gy/OtGeW7pWHvkR7pbpZRaClTN5aWXtdYLbvG92gINtdZPKqXq3mjVXJ7L87fy/Mxs37nW4Uopb2AG0B2IvWqVf4DaWuszSqkOwHylVIvLvqk7Y+Y7Os5QMLmVUh5AJPCZ1npfLqs43bHOQ2an+0zngdMd57zsNpfnjPxM5zXPLR9rQwqC1rpnPr5dANBBKXUA689TWSll0loHX7XeEaDWZY9rAsfyupN8znz5+2YopRZiPS2Nveq1TCDTtrxOKbUXaAwk5/G9HZ6ZOzzOtvcviNzTgL+11p9cZ5/OeKxvmBkn/UzfZJ/OeJxvxtk+00e48hJirnlu51i7/CUjrfXXWuvqWuu6WBtgdudSDAAWAsOUUl5KqXpAI6wNMw6nlCpluw7477fA3sDOXNarpGw9eZRS9bFmzu2bYoHLa2ac6Dj/Syn1FlAWeOIG6zjNsbZluGlmnPBY34yzHec8cqrjbGvzuKiU8rf1LroPuOYs47aO9a20QDviHzAAawXMBE4A0Ze9dgA4C6Ta1ml+1bZ1uaxVHegHvHHZ45ex9hDYRS6t8o7KDFQB1gKbgW3A54DH1ZmBQbbXN2FtWOzr7JkL8jjfZu6aWE+ndwAbbf/GOPmxzlNmZ/pM255/3/bYYvv/dWc+znnN7GyfadvzvsBWW6Yv+N+sE3d0rGXqCiGEEEAhuGQkhBAif0hBEEIIAUhBEEIIYSMFQQghBCAFQQghhI0UBCGEEIAUBCGEEDb/D5vcrTYyYiboAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 63 6.0 0 90.0 1.4356 232089.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 71 11.0 0 90.0 1.326 230503.8\n",
      "7 yoyos for tract,wedge,wedgePop,r= 73 0 206006.76012261273 1.1881\n",
      "8 yoyos for tract,wedge,wedgePop,r= 73 0 153619.25568289316 0.594\n",
      "9 yoyos for tract,wedge,wedgePop,r= 73 0 206312.05988210993 1.2095\n",
      "10 yoyos for tract,wedge,wedgePop,r= 73 0 164916.87972046007 0.9018\n",
      "10 yoyos for tract,wedge,wedgePop,r= 73 0 187440.68034923865 1.0557\n",
      "11 yoyos for tract,wedge,wedgePop,r= 73 0 204880.92569295043 1.1326\n",
      "I am working on tract number 80 of 1765 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 83 13.0 0 90.0 1.4738 237031.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 83 14.0 0 90.0 1.4213 237031.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 83 15.0 0 90.0 1.3707 237031.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 83 16.0 0 90.0 1.3219 237031.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 83 17.0 0 90.0 1.2748 237031.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 83 18.0 0 90.0 1.2294 237031.6\n",
      "7 yoyos for tract,wedge,wedgePop,r= 91 2 246717.86338893976 1.4316\n",
      "8 yoyos for tract,wedge,wedgePop,r= 91 2 117571.01881303925 0.7158\n",
      "9 yoyos for tract,wedge,wedgePop,r= 91 2 251701.02600677527 1.4708\n",
      "10 yoyos for tract,wedge,wedgePop,r= 91 2 151580.42737615702 1.0933\n",
      "11 yoyos for tract,wedge,wedgePop,r= 91 2 229251.4589031585 1.282\n",
      "12 yoyos for tract,wedge,wedgePop,r= 91 2 156846.77674404893 1.1876\n",
      "12 yoyos for tract,wedge,wedgePop,r= 91 2 194094.49490250775 1.2348\n",
      "13 yoyos for tract,wedge,wedgePop,r= 91 2 217115.8427282904 1.2584\n",
      "13 yoyos for tract,wedge,wedgePop,r= 91 2 207296.93520314604 1.2466\n",
      "loop31.0, tr91,wedgePops796093.51, 198431.6, 198484.4, 200758.4, 198419.1, Overedge?0, 0, 0, 0, ,Satisfied?1101,yoyo?00132 \n",
      "   targetWP, latest drx4 are tWP,dr, 198652.8,0.0053, 198652.8,0.0004, 198652.8,-0.0059, 198652.8,0.002\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 94 13.0 0 90.0 6.021 286650.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 94 14.0 0 90.0 4.508 286650.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 97 10.0 2 90.0 4.9747 281519.6\n",
      "I am working on tract number 100 of 1765 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 100 7.0 2 90.0 4.7493 294557.3\n",
      "we have 2 non-opposing shorted wedges for tract no 109\n",
      "I am working on tract number 120 of 1765 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 136 11.0 1 90.0 4.2161 283438.0\n",
      "7 yoyos for tract,wedge,wedgePop,r= 136 1 283437.9716843292 8.475\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 136 19.0 1 90.0 4.2375 283438.0\n",
      "7 yoyos for tract,wedge,wedgePop,r= 136 1 283437.9716843292 4.2375\n",
      "8 yoyos for tract,wedge,wedgePop,r= 136 1 99844.8308661409 2.1188\n",
      "8 yoyos for tract,wedge,wedgePop,r= 136 1 109904.57820341273 3.1791\n",
      "8 yoyos for tract,wedge,wedgePop,r= 136 1 143895.78182283993 3.7093\n",
      "9 yoyos for tract,wedge,wedgePop,r= 136 1 282883.04523325665 3.9744\n",
      "10 yoyos for tract,wedge,wedgePop,r= 136 1 165235.63837841045 3.8419\n",
      "11 yoyos for tract,wedge,wedgePop,r= 136 1 244140.9409582479 3.9081\n",
      "12 yoyos for tract,wedge,wedgePop,r= 136 1 175850.17639284127 3.875\n",
      "I am working on tract number 140 of 1765 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 144 1 208781.31893366532 2.9055\n",
      "8 yoyos for tract,wedge,wedgePop,r= 144 1 113776.17145817279 1.4528\n",
      "9 yoyos for tract,wedge,wedgePop,r= 144 1 218185.45458315057 2.9704\n",
      "10 yoyos for tract,wedge,wedgePop,r= 144 1 161997.6697007299 2.2116\n",
      "10 yoyos for tract,wedge,wedgePop,r= 144 1 179719.06075871087 2.591\n",
      "loop31.0, tr144,wedgePops782602.55, 198752.6, 186817.1, 198498.6, 198534.3, Overedge?0, 0, 0, 0, ,Satisfied?1011,yoyo?31000 \n",
      "   targetWP, latest drx4 are tWP,dr, 198652.8,-0.0151, 198652.8,0.1897, 198652.8,0.0004, 198652.8,0.0005\n",
      "10 yoyos for tract,wedge,wedgePop,r= 144 1 186817.09485463102 2.7807\n",
      "loop32.0, tr144,wedgePops795663.07, 198752.6, 199877.6, 198498.6, 198534.3, Overedge?0, 0, 0, 0, ,Satisfied?1011,yoyo?31000 \n",
      "   targetWP, latest drx4 are tWP,dr, 198652.8,-0.0151, 198652.8,0.0949, 198652.8,0.0004, 198652.8,0.0005\n",
      "7 yoyos for tract,wedge,wedgePop,r= 148 3 1316159.9205944983 4.3389\n",
      "7 yoyos for tract,wedge,wedgePop,r= 149 3 1079081.7872278937 3.1344\n",
      "8 yoyos for tract,wedge,wedgePop,r= 149 3 39931.612128794426 1.5672\n",
      "9 yoyos for tract,wedge,wedgePop,r= 149 3 1270430.6485995343 3.9308\n",
      "10 yoyos for tract,wedge,wedgePop,r= 149 3 430565.5142886671 2.749\n",
      "11 yoyos for tract,wedge,wedgePop,r= 149 3 1151408.1207671678 3.3399\n",
      "11 yoyos for tract,wedge,wedgePop,r= 149 3 1061330.1156149223 3.0445\n",
      "11 yoyos for tract,wedge,wedgePop,r= 149 3 862522.6310047263 2.8967\n",
      "11 yoyos for tract,wedge,wedgePop,r= 149 3 665971.5333796086 2.8229\n",
      "12 yoyos for tract,wedge,wedgePop,r= 149 3 536697.7096282904 2.7859\n",
      "13 yoyos for tract,wedge,wedgePop,r= 149 3 601719.181104989 2.8044\n",
      "14 yoyos for tract,wedge,wedgePop,r= 149 3 567495.873895278 2.7952\n",
      "7 yoyos for tract,wedge,wedgePop,r= 150 3 909324.6338391721 2.5529\n",
      "8 yoyos for tract,wedge,wedgePop,r= 150 3 21804.5866560461 1.2764\n",
      "9 yoyos for tract,wedge,wedgePop,r= 150 3 1007571.3679219655 2.6357\n",
      "10 yoyos for tract,wedge,wedgePop,r= 150 3 63973.76153414126 1.9561\n",
      "10 yoyos for tract,wedge,wedgePop,r= 150 3 225635.60155195606 2.2959\n",
      "11 yoyos for tract,wedge,wedgePop,r= 150 3 667494.6215971437 2.4658\n",
      "12 yoyos for tract,wedge,wedgePop,r= 150 3 370270.9888133306 2.3809\n",
      "12 yoyos for tract,wedge,wedgePop,r= 150 3 508362.7164798775 2.4233\n",
      "12 yoyos for tract,wedge,wedgePop,r= 150 3 596125.2843424222 2.4446\n",
      "7 yoyos for tract,wedge,wedgePop,r= 151 3 1128889.1872214088 3.0369\n",
      "8 yoyos for tract,wedge,wedgePop,r= 151 3 27182.525089704664 1.5185\n",
      "9 yoyos for tract,wedge,wedgePop,r= 151 3 1106607.441540587 2.9355\n",
      "10 yoyos for tract,wedge,wedgePop,r= 151 3 134996.05217811395 2.227\n",
      "11 yoyos for tract,wedge,wedgePop,r= 151 3 888143.6101694362 2.5813\n",
      "12 yoyos for tract,wedge,wedgePop,r= 151 3 360889.3983046296 2.4041\n",
      "13 yoyos for tract,wedge,wedgePop,r= 151 3 650379.7376512098 2.4927\n",
      "14 yoyos for tract,wedge,wedgePop,r= 151 3 486083.8458401109 2.4484\n",
      "14 yoyos for tract,wedge,wedgePop,r= 151 3 569894.9549873376 2.4706\n",
      "15 yoyos for tract,wedge,wedgePop,r= 151 3 613121.0362774527 2.4816\n",
      "16 yoyos for tract,wedge,wedgePop,r= 151 3 592236.1180306994 2.4761\n",
      "I am working on tract number 160 of 1765 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 162\n",
      "we have 2 non-opposing shorted wedges for tract no 166\n",
      "7 yoyos for tract,wedge,wedgePop,r= 168 3 1077708.728899125 3.0224\n",
      "8 yoyos for tract,wedge,wedgePop,r= 168 3 28110.263187751872 1.5112\n",
      "9 yoyos for tract,wedge,wedgePop,r= 168 3 1126632.7277340842 3.1708\n",
      "9 yoyos for tract,wedge,wedgePop,r= 168 3 641979.0560393361 2.341\n",
      "10 yoyos for tract,wedge,wedgePop,r= 168 3 63198.767550658435 1.9261\n",
      "10 yoyos for tract,wedge,wedgePop,r= 168 3 144669.46851121308 2.1336\n",
      "10 yoyos for tract,wedge,wedgePop,r= 168 3 290553.9704832714 2.2373\n",
      "10 yoyos for tract,wedge,wedgePop,r= 168 3 447552.5864446335 2.2892\n",
      "10 yoyos for tract,wedge,wedgePop,r= 168 3 554252.147451948 2.3151\n",
      "10 yoyos for tract,wedge,wedgePop,r= 168 3 598243.7077007816 2.328\n",
      "10 yoyos for tract,wedge,wedgePop,r= 168 3 618619.4346055054 2.3345\n",
      "10 yoyos for tract,wedge,wedgePop,r= 168 3 630125.941642295 2.3378\n",
      "7 yoyos for tract,wedge,wedgePop,r= 170 3 1302949.5453794193 4.0343\n",
      "7 yoyos for tract,wedge,wedgePop,r= 171 3 1172667.7016725081 3.4121\n",
      "8 yoyos for tract,wedge,wedgePop,r= 171 3 53433.35476900381 1.7061\n",
      "9 yoyos for tract,wedge,wedgePop,r= 171 3 1313125.2247114507 4.0895\n",
      "9 yoyos for tract,wedge,wedgePop,r= 171 3 717274.6101896482 2.8978\n",
      "10 yoyos for tract,wedge,wedgePop,r= 171 3 106719.96637572476 2.3019\n",
      "10 yoyos for tract,wedge,wedgePop,r= 171 3 183725.02612778952 2.5998\n",
      "10 yoyos for tract,wedge,wedgePop,r= 171 3 342793.157693401 2.7488\n",
      "10 yoyos for tract,wedge,wedgePop,r= 171 3 476692.8492674766 2.8233\n",
      "7 yoyos for tract,wedge,wedgePop,r= 172 3 1242337.3097616362 3.8222\n",
      "8 yoyos for tract,wedge,wedgePop,r= 172 3 77469.98137747613 1.9111\n",
      "9 yoyos for tract,wedge,wedgePop,r= 172 3 1245496.8971776774 3.846\n",
      "10 yoyos for tract,wedge,wedgePop,r= 172 3 543032.9716330878 2.8786\n",
      "11 yoyos for tract,wedge,wedgePop,r= 172 3 1177209.713531291 3.3623\n",
      "11 yoyos for tract,wedge,wedgePop,r= 172 3 1095737.2341034154 3.1204\n",
      "11 yoyos for tract,wedge,wedgePop,r= 172 3 906958.9431578175 2.9995\n",
      "11 yoyos for tract,wedge,wedgePop,r= 172 3 743534.8461252854 2.939\n",
      "11 yoyos for tract,wedge,wedgePop,r= 172 3 641685.9180020543 2.9088\n",
      "11 yoyos for tract,wedge,wedgePop,r= 172 3 589845.6574267413 2.8937\n",
      "11 yoyos for tract,wedge,wedgePop,r= 172 3 565285.7975349535 2.8861\n",
      "12 yoyos for tract,wedge,wedgePop,r= 172 3 554192.2950663973 2.8823\n",
      "7 yoyos for tract,wedge,wedgePop,r= 173 3 1187154.3560025776 3.6183\n",
      "8 yoyos for tract,wedge,wedgePop,r= 173 3 53139.54763517855 1.8092\n",
      "9 yoyos for tract,wedge,wedgePop,r= 173 3 1330377.230359111 4.1168\n",
      "9 yoyos for tract,wedge,wedgePop,r= 173 3 966192.4892944782 2.963\n",
      "10 yoyos for tract,wedge,wedgePop,r= 173 3 106546.77411943395 2.3861\n",
      "10 yoyos for tract,wedge,wedgePop,r= 173 3 289449.62194085884 2.6745\n",
      "11 yoyos for tract,wedge,wedgePop,r= 173 3 580294.9857690122 2.8188\n",
      "12 yoyos for tract,wedge,wedgePop,r= 173 3 387277.4018774334 2.7466\n",
      "12 yoyos for tract,wedge,wedgePop,r= 173 3 467792.3583916627 2.7827\n",
      "12 yoyos for tract,wedge,wedgePop,r= 173 3 520663.4770500191 2.8007\n",
      "12 yoyos for tract,wedge,wedgePop,r= 173 3 550066.6343838465 2.8097\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 174 15.0 3 84.5 3.9646 1325422.4\n",
      "I am working on tract number 180 of 1765 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 196 5.0 1 90.0 4.3689 205964.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 196 6.0 1 90.0 4.2515 205964.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 196 7.0 1 90.0 4.1373 205964.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 196 8.0 1 90.0 4.0262 205964.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 196 9.0 1 90.0 3.918 205964.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 196 10.0 1 90.0 3.8127 205964.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 196 11.0 1 90.0 3.7103 205964.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 196 12.0 1 90.0 3.6106 205964.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 196 17.0 1 90.0 4.9201 205964.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 196 18.0 1 90.0 4.7879 205964.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 196 19.0 1 90.0 4.6593 205964.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 196 20.0 1 90.0 4.5341 205964.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 196 21.0 1 90.0 4.4123 205964.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 196 22.0 1 90.0 4.2937 205964.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 196 23.0 1 90.0 4.1784 205964.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 196 24.0 1 90.0 4.0661 205964.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 196 25.0 1 90.0 3.9569 205964.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 196 26.0 1 90.0 3.8506 205964.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 196 27.0 1 90.0 3.7471 205964.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 196 28.0 1 90.0 3.6464 205964.8\n",
      "loop31.0, tr196,wedgePops667725.78, 198584.6, 72139.5, 198421.8, 198579.8, Overedge?0, 0, 0, 0, ,Satisfied?1011,yoyo?4422 \n",
      "   targetWP, latest drx4 are tWP,dr, 198652.8,0.0011, 198652.8,1.3691, 198652.8,0.0005, 198652.8,0.0145\n",
      "loop32.0, tr196,wedgePops801551.05, 198584.6, 205964.8, 198421.8, 198579.8, Overedge?0, 0, 0, 0, ,Satisfied?1011,yoyo?4422 \n",
      "   targetWP, latest drx4 are tWP,dr, 198652.8,0.0011, 198652.8,3.499, 198652.8,0.0005, 198652.8,0.0145\n",
      "loop33.0, tr196,wedgePops801551.05, 198584.6, 205964.8, 198421.8, 198579.8, Overedge?0, 0, 0, 0, ,Satisfied?1011,yoyo?4522 \n",
      "   targetWP, latest drx4 are tWP,dr, 198652.8,0.0011, 198652.8,-0.1139, 198652.8,0.0005, 198652.8,0.0145\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 196 33.0 1 90.0 6.5285 205964.8\n",
      "loop34.0, tr196,wedgePops801551.05, 198584.6, 205964.8, 198421.8, 198579.8, Overedge?0, 0, 0, 0, ,Satisfied?1011,yoyo?4522 \n",
      "   targetWP, latest drx4 are tWP,dr, 198652.8,0.0011, 198652.8,-0.1754, 198652.8,0.0005, 198652.8,0.0145\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 196 34.0 1 90.0 6.3531 205964.8\n",
      "loop35.0, tr196,wedgePops801551.05, 198584.6, 205964.8, 198421.8, 198579.8, Overedge?0, 0, 0, 0, ,Satisfied?1011,yoyo?4522 \n",
      "   targetWP, latest drx4 are tWP,dr, 198652.8,0.0011, 198652.8,-0.1707, 198652.8,0.0005, 198652.8,0.0145\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 196 35.0 1 90.0 6.1824 205964.8\n",
      "loop36.0, tr196,wedgePops801551.05, 198584.6, 205964.8, 198421.8, 198579.8, Overedge?0, 0, 0, 0, ,Satisfied?1011,yoyo?4522 \n",
      "   targetWP, latest drx4 are tWP,dr, 198652.8,0.0011, 198652.8,-0.1661, 198652.8,0.0005, 198652.8,0.0145\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 196 36.0 1 90.0 6.0163 205964.8\n",
      "loop37.0, tr196,wedgePops801551.05, 198584.6, 205964.8, 198421.8, 198579.8, Overedge?0, 0, 0, 0, ,Satisfied?1011,yoyo?4522 \n",
      "   targetWP, latest drx4 are tWP,dr, 198652.8,0.0011, 198652.8,-0.1616, 198652.8,0.0005, 198652.8,0.0145\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 196 37.0 1 90.0 5.8547 205964.8\n",
      "loop38.0, tr196,wedgePops801551.05, 198584.6, 205964.8, 198421.8, 198579.8, Overedge?0, 0, 0, 0, ,Satisfied?1011,yoyo?4522 \n",
      "   targetWP, latest drx4 are tWP,dr, 198652.8,0.0011, 198652.8,-0.1573, 198652.8,0.0005, 198652.8,0.0145\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 196 38.0 1 90.0 5.6974 205964.8\n",
      "loop39.0, tr196,wedgePops801551.05, 198584.6, 205964.8, 198421.8, 198579.8, Overedge?0, 0, 0, 0, ,Satisfied?1011,yoyo?4522 \n",
      "   targetWP, latest drx4 are tWP,dr, 198652.8,0.0011, 198652.8,-0.1531, 198652.8,0.0005, 198652.8,0.0145\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 0 1.24463 197725.087 198584.6279 0\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 1 5.54431 205964.7682 205964.7682 0\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 2 0.20075 197443.588 198421.8126 0\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 3 0.75974 197740.2851 198579.8423 0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 196 39.0 1 90.0 5.5443 205964.8\n",
      "loop40.0, tr196,wedgePops801551.05, 198584.6, 205964.8, 198421.8, 198579.8, Overedge?0, 0, 0, 0, ,Satisfied?1011,yoyo?4522 \n",
      "   targetWP, latest drx4 are tWP,dr, 198652.8,0.0011, 198652.8,-0.149, 198652.8,0.0005, 198652.8,0.0145\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 0 1.24463 197725.087 198584.6279 0\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 1 5.39535 205964.7682 205964.7682 0\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 2 0.20075 197443.588 198421.8126 0\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 3 0.75974 197740.2851 198579.8423 0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I looped 40 times on tract 196, giving up w pop 801551.0510614141\n",
      "7 yoyos for tract,wedge,wedgePop,r= 197 2 217273.5980920464 1.3252\n",
      "8 yoyos for tract,wedge,wedgePop,r= 197 2 167380.99796067525 0.6626\n",
      "9 yoyos for tract,wedge,wedgePop,r= 197 2 217271.99898500193 1.3247\n",
      "10 yoyos for tract,wedge,wedgePop,r= 197 2 180185.0293955902 0.9936\n",
      "11 yoyos for tract,wedge,wedgePop,r= 197 2 213513.05197829194 1.1592\n",
      "I am working on tract number 200 of 1765 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 203 0 219203.4521821715 1.2886\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 206 7.0 1 90.0 4.419 257118.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 206 12.0 1 90.0 4.1316 257118.7\n",
      "7 yoyos for tract,wedge,wedgePop,r= 206 2 228696.0236494611 1.3012\n",
      "loop31.0, tr206,wedgePops733777.29, 198200.9, 198451.6, 138738.6, 198386.2, Overedge?0, 0, 0, 0, ,Satisfied?1101,yoyo?0071 \n",
      "   targetWP, latest drx4 are tWP,dr, 198652.8,0.0079, 198652.8,0.0005, 198652.8,-0.6506, 198652.8,-0.4696\n",
      "8 yoyos for tract,wedge,wedgePop,r= 206 2 138738.62418955896 0.6506\n",
      "loop32.0, tr206,wedgePops842667.06, 198200.9, 198451.6, 247628.4, 198386.2, Overedge?0, 0, 0, 0, ,Satisfied?1101,yoyo?0081 \n",
      "   targetWP, latest drx4 are tWP,dr, 198652.8,0.0079, 198652.8,0.0005, 198652.8,0.776, 198652.8,-0.4696\n",
      "9 yoyos for tract,wedge,wedgePop,r= 206 2 247628.38943969406 1.4266\n",
      "loop33.0, tr206,wedgePops759743.27, 198200.9, 198451.6, 164704.6, 198386.2, Overedge?0, 0, 0, 0, ,Satisfied?1101,yoyo?0091 \n",
      "   targetWP, latest drx4 are tWP,dr, 198652.8,0.0079, 198652.8,0.0005, 198652.8,-0.388, 198652.8,-0.4696\n",
      "10 yoyos for tract,wedge,wedgePop,r= 206 2 164704.59967442747 1.0386\n",
      "loop34.0, tr206,wedgePops769362.61, 198200.9, 198451.6, 174323.9, 198386.2, Overedge?0, 0, 0, 0, ,Satisfied?1101,yoyo?00101 \n",
      "   targetWP, latest drx4 are tWP,dr, 198652.8,0.0079, 198652.8,0.0005, 198652.8,0.194, 198652.8,-0.4696\n",
      "10 yoyos for tract,wedge,wedgePop,r= 206 2 174323.9418014098 1.2326\n",
      "loop35.0, tr206,wedgePops832321.99, 198200.9, 198451.6, 237283.3, 198386.2, Overedge?0, 0, 0, 0, ,Satisfied?1101,yoyo?00101 \n",
      "   targetWP, latest drx4 are tWP,dr, 198652.8,0.0079, 198652.8,0.0005, 198652.8,0.097, 198652.8,-0.4696\n",
      "11 yoyos for tract,wedge,wedgePop,r= 206 2 237283.32781711125 1.3296\n",
      "loop36.0, tr206,wedgePops809282.51, 198200.9, 198451.6, 214243.8, 198386.2, Overedge?0, 0, 0, 0, ,Satisfied?1101,yoyo?00111 \n",
      "   targetWP, latest drx4 are tWP,dr, 198652.8,0.0079, 198652.8,0.0005, 198652.8,-0.0485, 198652.8,-0.4696\n",
      "11 yoyos for tract,wedge,wedgePop,r= 206 2 214243.84258815402 1.2811\n",
      "loop37.0, tr206,wedgePops783342.88, 198200.9, 198451.6, 188304.2, 198386.2, Overedge?0, 0, 0, 0, ,Satisfied?1101,yoyo?00111 \n",
      "   targetWP, latest drx4 are tWP,dr, 198652.8,0.0079, 198652.8,0.0005, 198652.8,-0.0243, 198652.8,-0.4696\n",
      "12 yoyos for tract,wedge,wedgePop,r= 206 2 188304.20971268826 1.2569\n",
      "loop38.0, tr206,wedgePops796841.24, 198200.9, 198451.6, 201802.6, 198386.2, Overedge?0, 0, 0, 0, ,Satisfied?1101,yoyo?00121 \n",
      "   targetWP, latest drx4 are tWP,dr, 198652.8,0.0079, 198652.8,0.0005, 198652.8,0.0121, 198652.8,-0.4696\n",
      "I am working on tract number 220 of 1765 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 220 5.0 1 90.0 3.3141 226591.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 220 6.0 1 90.0 2.9976 226591.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 220 11.0 1 90.0 3.5276 226591.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 220 12.0 1 90.0 3.1907 226591.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 220 13.0 1 90.0 2.8859 226591.8\n",
      "7 yoyos for tract,wedge,wedgePop,r= 220 1 226591.77612879832 4.3756\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 222 7.0 3 90.0 3.0451 309405.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 223 6.0 1 90.0 3.2223 256523.9\n",
      "I am working on tract number 240 of 1765 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 254 2 381724.89662946516 2.0135\n",
      "8 yoyos for tract,wedge,wedgePop,r= 254 2 142957.04294382586 1.0067\n",
      "9 yoyos for tract,wedge,wedgePop,r= 254 2 381264.68017524236 2.0115\n",
      "9 yoyos for tract,wedge,wedgePop,r= 254 2 311806.61447345815 1.5091\n",
      "10 yoyos for tract,wedge,wedgePop,r= 254 2 158912.69802228655 1.2579\n",
      "11 yoyos for tract,wedge,wedgePop,r= 254 2 226888.59500244574 1.3835\n",
      "12 yoyos for tract,wedge,wedgePop,r= 254 2 178564.12047190045 1.3207\n",
      "7 yoyos for tract,wedge,wedgePop,r= 257 3 255568.50868897018 0.5473\n",
      "8 yoyos for tract,wedge,wedgePop,r= 257 3 175619.74078069546 0.2736\n",
      "9 yoyos for tract,wedge,wedgePop,r= 257 3 255040.9194905797 0.5457\n",
      "9 yoyos for tract,wedge,wedgePop,r= 257 3 227371.9675583773 0.4097\n",
      "10 yoyos for tract,wedge,wedgePop,r= 257 3 190754.3082597031 0.3417\n",
      "11 yoyos for tract,wedge,wedgePop,r= 257 3 217828.4429502881 0.3757\n",
      "I am working on tract number 260 of 1765 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 264 3 428485.0411031671 2.1548\n",
      "8 yoyos for tract,wedge,wedgePop,r= 264 3 115101.71661201725 1.0774\n",
      "9 yoyos for tract,wedge,wedgePop,r= 264 3 430456.3038547194 2.1806\n",
      "9 yoyos for tract,wedge,wedgePop,r= 264 3 318407.4203826266 1.629\n",
      "9 yoyos for tract,wedge,wedgePop,r= 264 3 223430.28747665638 1.3532\n",
      "10 yoyos for tract,wedge,wedgePop,r= 264 3 125703.7360145001 1.2153\n",
      "10 yoyos for tract,wedge,wedgePop,r= 264 3 158540.9073314842 1.2843\n",
      "10 yoyos for tract,wedge,wedgePop,r= 264 3 184005.40698867216 1.3187\n",
      "11 yoyos for tract,wedge,wedgePop,r= 264 3 204486.57374784973 1.336\n",
      "12 yoyos for tract,wedge,wedgePop,r= 264 3 193802.5943659902 1.3273\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 270 15.0 0 90.0 2.4135 210834.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 270 16.0 0 90.0 2.3073 210834.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 270 21.0 0 90.0 2.4075 210834.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 270 22.0 0 90.0 2.3016 210834.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 270 27.0 0 90.0 2.4065 210834.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 270 28.0 0 90.0 2.3006 210834.8\n",
      "loop31.0, tr270,wedgePops767101.52, 171037.4, 198461.0, 198540.8, 199062.2, Overedge?0, 0, 0, 0, ,Satisfied?0111,yoyo?6001 \n",
      "   targetWP, latest drx4 are tWP,dr, 198652.8,0.731, 198652.8,0.0012, 198652.8,0.0002, 198652.8,-0.0006\n",
      "loop32.0, tr270,wedgePops806898.86, 210834.8, 198461.0, 198540.8, 199062.2, Overedge?0, 0, 0, 0, ,Satisfied?0111,yoyo?6001 \n",
      "   targetWP, latest drx4 are tWP,dr, 198652.8,0.7363, 198652.8,0.0012, 198652.8,0.0002, 198652.8,-0.0006\n",
      "7 yoyos for tract,wedge,wedgePop,r= 270 0 210834.76527253614 2.567\n",
      "loop33.0, tr270,wedgePops755386.13, 159322.0, 198461.0, 198540.8, 199062.2, Overedge?1, 0, 0, 0, ,Satisfied?0111,yoyo?0000 \n",
      "   targetWP, latest drx4 are tWP,dr, 211763.0,-1.2835, 211763.0,0.0012, 211763.0,0.0002, 211763.0,-0.0006\n",
      "loop34.0, tr270,wedgePops787024.41, 159322.0, 208184.9, 209281.3, 210236.1, Overedge?1, 0, 0, 0, ,Satisfied?0000,yoyo?0001 \n",
      "   targetWP, latest drx4 are tWP,dr, 211763.0,-1.2835, 211763.0,0.0182, 211763.0,0.0038, 211763.0,0.0065\n",
      "loop35.0, tr270,wedgePops793548.96, 159322.0, 211336.6, 211287.5, 211602.8, Overedge?1, 0, 0, 0, ,Satisfied?0000,yoyo?0001 \n",
      "   targetWP, latest drx4 are tWP,dr, 211763.0,-1.2835, 211763.0,0.0051, 211763.0,0.0007, 211763.0,0.0007\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 271 6.0 0 90.0 3.1145 280609.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 271 14.0 0 90.0 3.9347 280609.9\n",
      "7 yoyos for tract,wedge,wedgePop,r= 271 0 280609.9331415928 4.4893\n",
      "I am working on tract number 280 of 1765 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 291 3 231841.35149090306 1.2822\n",
      "8 yoyos for tract,wedge,wedgePop,r= 291 3 177066.19207812648 0.6411\n",
      "9 yoyos for tract,wedge,wedgePop,r= 291 3 232523.29364555088 1.286\n",
      "10 yoyos for tract,wedge,wedgePop,r= 291 3 180805.1815367947 0.9636\n",
      "10 yoyos for tract,wedge,wedgePop,r= 291 3 186419.10745271522 1.1248\n",
      "11 yoyos for tract,wedge,wedgePop,r= 291 3 221592.2692212906 1.2054\n",
      "I am working on tract number 300 of 1765 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 312 12.0 0 90.0 3.8037 274215.6\n",
      "I am working on tract number 320 of 1765 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 324 0 285347.06400341087 3.372\n",
      "we have 2 non-opposing shorted wedges for tract no 336\n",
      "I am working on tract number 340 of 1765 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 343 12.0 3 90.0 3.983 270162.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 343 13.0 3 90.0 3.1316 270162.1\n",
      "7 yoyos for tract,wedge,wedgePop,r= 343 3 270162.11084200686 3.2054\n",
      "8 yoyos for tract,wedge,wedgePop,r= 343 3 89577.87048700277 1.6027\n",
      "9 yoyos for tract,wedge,wedgePop,r= 343 3 270162.11084200686 3.9212\n",
      "I am working on tract number 360 of 1765 tracts\n",
      "I am working on tract number 380 of 1765 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 390 11.0 0 90.0 1.2439 199345.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 390 12.0 0 90.0 1.2407 199345.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 390 13.0 0 90.0 1.2374 199345.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 391 14.0 1 90.0 5.8795 278201.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 391 15.0 1 90.0 4.5127 278201.4\n",
      "I am working on tract number 400 of 1765 tracts\n",
      "I am working on tract number 420 of 1765 tracts\n",
      "I am working on tract number 440 of 1765 tracts\n",
      "I am working on tract number 460 of 1765 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 479 3.0 1 90.0 0.7248 63958.3\n",
      "I am working on tract number 480 of 1765 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 480\n",
      "I am working on tract number 500 of 1765 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 500 7.0 1 90.0 1.4434 216085.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 500 16.0 1 90.0 1.4054 216085.6\n",
      "7 yoyos for tract,wedge,wedgePop,r= 504 2 350283.57819655724 2.03\n",
      "8 yoyos for tract,wedge,wedgePop,r= 504 2 121056.63210879706 1.015\n",
      "9 yoyos for tract,wedge,wedgePop,r= 504 2 349968.8840031226 2.0238\n",
      "9 yoyos for tract,wedge,wedgePop,r= 504 2 300756.1411479128 1.5194\n",
      "10 yoyos for tract,wedge,wedgePop,r= 504 2 170047.9659068213 1.2672\n",
      "11 yoyos for tract,wedge,wedgePop,r= 504 2 256448.45866410548 1.3933\n",
      "11 yoyos for tract,wedge,wedgePop,r= 504 2 225929.39979013026 1.3303\n",
      "7 yoyos for tract,wedge,wedgePop,r= 508 2 361757.10129188595 1.9672\n",
      "8 yoyos for tract,wedge,wedgePop,r= 508 2 132817.3125159744 0.9836\n",
      "9 yoyos for tract,wedge,wedgePop,r= 508 2 361724.9155951909 1.9662\n",
      "9 yoyos for tract,wedge,wedgePop,r= 508 2 285135.8997846803 1.4749\n",
      "10 yoyos for tract,wedge,wedgePop,r= 508 2 182986.4896837643 1.2293\n",
      "11 yoyos for tract,wedge,wedgePop,r= 508 2 253094.92389971722 1.3521\n",
      "11 yoyos for tract,wedge,wedgePop,r= 508 2 216156.83913589624 1.2907\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 511 7.0 2 90.0 2.5506 313795.6\n",
      "I am working on tract number 520 of 1765 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 530 2 453349.53398642 2.1012\n",
      "8 yoyos for tract,wedge,wedgePop,r= 530 2 141721.2164456711 1.0506\n",
      "9 yoyos for tract,wedge,wedgePop,r= 530 2 375692.2908319421 1.9779\n",
      "9 yoyos for tract,wedge,wedgePop,r= 530 2 325124.197988355 1.5143\n",
      "10 yoyos for tract,wedge,wedgePop,r= 530 2 181988.65412765468 1.2824\n",
      "11 yoyos for tract,wedge,wedgePop,r= 530 2 274903.8536396675 1.3983\n",
      "11 yoyos for tract,wedge,wedgePop,r= 530 2 229535.13730199833 1.3404\n",
      "7 yoyos for tract,wedge,wedgePop,r= 537 1 444374.7438740501 2.2283\n",
      "8 yoyos for tract,wedge,wedgePop,r= 537 1 125530.7455300088 1.1141\n",
      "9 yoyos for tract,wedge,wedgePop,r= 537 1 444518.09636879293 2.2308\n",
      "9 yoyos for tract,wedge,wedgePop,r= 537 1 330273.1089828535 1.6725\n",
      "9 yoyos for tract,wedge,wedgePop,r= 537 1 237252.38135884894 1.3933\n",
      "10 yoyos for tract,wedge,wedgePop,r= 537 1 138196.18557906215 1.2537\n",
      "10 yoyos for tract,wedge,wedgePop,r= 537 1 170123.59210164534 1.3235\n",
      "10 yoyos for tract,wedge,wedgePop,r= 537 1 194984.9816982096 1.3584\n",
      "11 yoyos for tract,wedge,wedgePop,r= 537 1 216713.861929305 1.3759\n",
      "11 yoyos for tract,wedge,wedgePop,r= 537 1 205503.15190683794 1.3671\n",
      "I am working on tract number 540 of 1765 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 559 9.0 0 90.0 3.7339 230923.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 559 10.0 0 90.0 3.3279 230923.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 559 11.0 0 90.0 2.966 230923.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 559 12.0 0 90.0 2.6434 230923.3\n",
      "I am working on tract number 560 of 1765 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 578 0 208469.52882312462 2.7066\n",
      "8 yoyos for tract,wedge,wedgePop,r= 578 0 110832.51897927701 1.3533\n",
      "9 yoyos for tract,wedge,wedgePop,r= 578 0 229993.74928931435 2.7176\n",
      "10 yoyos for tract,wedge,wedgePop,r= 578 0 122714.4664984776 2.0355\n",
      "10 yoyos for tract,wedge,wedgePop,r= 578 0 141259.3837370432 2.3766\n",
      "10 yoyos for tract,wedge,wedgePop,r= 578 0 165868.17442893682 2.5471\n",
      "10 yoyos for tract,wedge,wedgePop,r= 578 0 173874.34204862197 2.6324\n",
      "10 yoyos for tract,wedge,wedgePop,r= 578 0 185540.58582624144 2.675\n",
      "I am working on tract number 580 of 1765 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 598 2 266518.5057259919 1.0276\n",
      "8 yoyos for tract,wedge,wedgePop,r= 598 2 22600.336987879244 0.5138\n",
      "9 yoyos for tract,wedge,wedgePop,r= 598 2 792155.2565297585 1.2836\n",
      "10 yoyos for tract,wedge,wedgePop,r= 598 2 87775.9528519737 0.8987\n",
      "11 yoyos for tract,wedge,wedgePop,r= 598 2 438937.3048674005 1.0912\n",
      "12 yoyos for tract,wedge,wedgePop,r= 598 2 199618.59372850388 0.9949\n",
      "13 yoyos for tract,wedge,wedgePop,r= 598 2 303502.6084244354 1.0431\n",
      "13 yoyos for tract,wedge,wedgePop,r= 598 2 246792.9129866419 1.019\n",
      "7 yoyos for tract,wedge,wedgePop,r= 599 2 1010771.7956377367 1.8626\n",
      "8 yoyos for tract,wedge,wedgePop,r= 599 2 82307.52556543145 0.9313\n",
      "9 yoyos for tract,wedge,wedgePop,r= 599 2 1006002.1168910569 1.7845\n",
      "9 yoyos for tract,wedge,wedgePop,r= 599 2 816935.6061748749 1.3579\n",
      "9 yoyos for tract,wedge,wedgePop,r= 599 2 404631.34242193913 1.1446\n",
      "10 yoyos for tract,wedge,wedgePop,r= 599 2 183449.3799671727 1.038\n",
      "10 yoyos for tract,wedge,wedgePop,r= 599 2 273562.4516029946 1.0913\n",
      "11 yoyos for tract,wedge,wedgePop,r= 599 2 335443.2544241011 1.118\n",
      "11 yoyos for tract,wedge,wedgePop,r= 599 2 304280.06929922913 1.1046\n",
      "loop31.0, tr599,wedgePops786312.72, 18649.5, 44712.1, 288784.1, 434167.1, Overedge?1, 1, 0, 1, ,Satisfied?0000,yoyo?00110 \n",
      "   targetWP, latest drx4 are tWP,dr, 984355.5,0.4573, 984355.5,1.7676, 297082.4,-0.0067, 984355.5,1.7676\n",
      "12 yoyos for tract,wedge,wedgePop,r= 599 2 288784.05908426264 1.098\n",
      "loop32.0, tr599,wedgePops793993.25, 18649.5, 44712.1, 296464.6, 434167.1, Overedge?1, 1, 0, 1, ,Satisfied?0000,yoyo?00120 \n",
      "   targetWP, latest drx4 are tWP,dr, 984355.5,0.4573, 984355.5,1.7676, 297082.4,0.0033, 984355.5,1.7676\n",
      "I am working on tract number 600 of 1765 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 600\n",
      "7 yoyos for tract,wedge,wedgePop,r= 603 1 539228.0152046797 4.2763\n",
      "8 yoyos for tract,wedge,wedgePop,r= 603 1 157533.7179554007 2.1381\n",
      "9 yoyos for tract,wedge,wedgePop,r= 603 1 539212.3097844173 4.2755\n",
      "10 yoyos for tract,wedge,wedgePop,r= 603 1 296056.97992007586 3.2068\n",
      "10 yoyos for tract,wedge,wedgePop,r= 603 1 380744.44916282356 3.7412\n",
      "11 yoyos for tract,wedge,wedgePop,r= 603 1 527881.4092730049 4.0083\n",
      "11 yoyos for tract,wedge,wedgePop,r= 603 1 456995.50457180256 3.8747\n",
      "12 yoyos for tract,wedge,wedgePop,r= 603 1 395821.7326710974 3.808\n",
      "12 yoyos for tract,wedge,wedgePop,r= 603 1 405650.0004201708 3.8414\n",
      "loop31.0, tr603,wedgePops766689.67, 58182.3, 424963.0, 58133.7, 225410.7, Overedge?1, 0, 0, 1, ,Satisfied?0010,yoyo?01220 \n",
      "   targetWP, latest drx4 are tWP,dr, 452835.8,1.7676, 452835.8,0.0167, 58182.3,0.0013, 452835.8,1.7676\n",
      "12 yoyos for tract,wedge,wedgePop,r= 603 1 424962.98490344256 3.8581\n",
      "loop32.0, tr603,wedgePops783222.69, 58182.3, 441496.0, 58133.7, 225410.7, Overedge?1, 0, 0, 1, ,Satisfied?0010,yoyo?01220 \n",
      "   targetWP, latest drx4 are tWP,dr, 452835.8,1.7676, 452835.8,0.0083, 58182.3,0.0013, 452835.8,1.7676\n",
      "12 yoyos for tract,wedge,wedgePop,r= 603 1 441496.00110565906 3.8664\n",
      "loop33.0, tr603,wedgePops792053.15, 58182.3, 450326.5, 58133.7, 225410.7, Overedge?1, 0, 0, 1, ,Satisfied?0010,yoyo?01220 \n",
      "   targetWP, latest drx4 are tWP,dr, 452835.8,1.7676, 452835.8,0.0042, 58182.3,0.0013, 452835.8,1.7676\n",
      "we have 2 non-opposing shorted wedges for tract no 604\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 608 3.0 2 90.0 3.3011 219882.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 608 10.0 2 90.0 3.9761 219882.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 608 11.0 2 90.0 3.6809 219882.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 608 12.0 2 90.0 3.4076 219882.9\n",
      "7 yoyos for tract,wedge,wedgePop,r= 608 2 219882.91410665243 4.066\n",
      "7 yoyos for tract,wedge,wedgePop,r= 614 1 405446.1834503905 2.3734\n",
      "8 yoyos for tract,wedge,wedgePop,r= 614 1 190936.36185618618 1.1867\n",
      "9 yoyos for tract,wedge,wedgePop,r= 614 1 435291.40138687403 2.4122\n",
      "10 yoyos for tract,wedge,wedgePop,r= 614 1 250776.94810649147 1.7995\n",
      "10 yoyos for tract,wedge,wedgePop,r= 614 1 263352.85094238556 2.1058\n",
      "10 yoyos for tract,wedge,wedgePop,r= 614 1 284073.66452942084 2.259\n",
      "11 yoyos for tract,wedge,wedgePop,r= 614 1 334519.2030514682 2.3356\n",
      "12 yoyos for tract,wedge,wedgePop,r= 614 1 303020.4063743264 2.2973\n",
      "12 yoyos for tract,wedge,wedgePop,r= 614 1 317694.7986226328 2.3165\n",
      "7 yoyos for tract,wedge,wedgePop,r= 617 1 273953.3164145608 1.0431\n",
      "8 yoyos for tract,wedge,wedgePop,r= 617 1 67276.56942169921 0.5215\n",
      "9 yoyos for tract,wedge,wedgePop,r= 617 1 1625033.7046768006 1.5044\n",
      "I am working on tract number 620 of 1765 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 625 3 853317.0379467228 2.5112\n",
      "8 yoyos for tract,wedge,wedgePop,r= 625 3 98223.5819003518 1.2556\n",
      "9 yoyos for tract,wedge,wedgePop,r= 625 3 863760.4720765222 2.5753\n",
      "9 yoyos for tract,wedge,wedgePop,r= 625 3 744692.2525169025 1.9154\n",
      "9 yoyos for tract,wedge,wedgePop,r= 625 3 517770.95490668196 1.5855\n",
      "9 yoyos for tract,wedge,wedgePop,r= 625 3 213975.39883302717 1.4206\n",
      "10 yoyos for tract,wedge,wedgePop,r= 625 3 116142.16248594159 1.3381\n",
      "10 yoyos for tract,wedge,wedgePop,r= 625 3 152172.81735201448 1.3793\n",
      "10 yoyos for tract,wedge,wedgePop,r= 625 3 182375.93026094817 1.3999\n",
      "7 yoyos for tract,wedge,wedgePop,r= 628 1 390071.0033458072 3.9401\n",
      "8 yoyos for tract,wedge,wedgePop,r= 628 1 525772.3582353331 8.3871\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 628 17.0 1 90.0 6.1636 525772.4\n",
      "8 yoyos for tract,wedge,wedgePop,r= 628 1 525772.3582353331 6.1636\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 628 18.0 1 90.0 5.0519 525772.4\n",
      "8 yoyos for tract,wedge,wedgePop,r= 628 1 525772.3582353331 5.0519\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 628 19.0 1 90.0 4.496 525772.4\n",
      "8 yoyos for tract,wedge,wedgePop,r= 628 1 525772.3582353331 4.496\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 628 20.0 1 90.0 4.2181 525772.4\n",
      "8 yoyos for tract,wedge,wedgePop,r= 628 1 525772.3582353331 4.2181\n",
      "8 yoyos for tract,wedge,wedgePop,r= 628 1 466261.2148362732 4.0791\n",
      "9 yoyos for tract,wedge,wedgePop,r= 628 1 400762.12591989915 4.0096\n",
      "9 yoyos for tract,wedge,wedgePop,r= 628 1 413148.6048260264 4.0443\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 631 7.0 3 90.0 2.5827 233205.5\n",
      "I am working on tract number 640 of 1765 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 657\n",
      "we have 2 non-opposing shorted wedges for tract no 658\n",
      "I am working on tract number 660 of 1765 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 678\n",
      "I am working on tract number 680 of 1765 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 696 0 203691.283010682 1.674\n",
      "7 yoyos for tract,wedge,wedgePop,r= 696 1 273307.2497094794 2.7638\n",
      "8 yoyos for tract,wedge,wedgePop,r= 696 0 55823.057776922215 0.837\n",
      "8 yoyos for tract,wedge,wedgePop,r= 696 1 41342.5769787404 1.3819\n",
      "9 yoyos for tract,wedge,wedgePop,r= 696 0 224469.95914078056 1.7297\n",
      "9 yoyos for tract,wedge,wedgePop,r= 696 1 273317.1820144688 2.764\n",
      "10 yoyos for tract,wedge,wedgePop,r= 696 0 157018.3046772995 1.2833\n",
      "10 yoyos for tract,wedge,wedgePop,r= 696 1 97780.19612718082 2.073\n",
      "10 yoyos for tract,wedge,wedgePop,r= 696 0 163886.9603692458 1.5065\n",
      "10 yoyos for tract,wedge,wedgePop,r= 696 1 179371.5073442231 2.4185\n",
      "10 yoyos for tract,wedge,wedgePop,r= 696 0 169587.5808309159 1.6181\n",
      "11 yoyos for tract,wedge,wedgePop,r= 696 1 265712.4455005042 2.5912\n",
      "11 yoyos for tract,wedge,wedgePop,r= 696 0 203677.82898658054 1.6739\n",
      "11 yoyos for tract,wedge,wedgePop,r= 696 1 261615.94988335113 2.5049\n",
      "12 yoyos for tract,wedge,wedgePop,r= 696 0 182782.72096191358 1.646\n",
      "11 yoyos for tract,wedge,wedgePop,r= 696 1 237594.33594690813 2.4617\n",
      "12 yoyos for tract,wedge,wedgePop,r= 696 0 196457.07010370374 1.66\n",
      "11 yoyos for tract,wedge,wedgePop,r= 696 1 213092.05303827362 2.4401\n",
      "I am working on tract number 700 of 1765 tracts\n",
      "I am working on tract number 720 of 1765 tracts\n",
      "I am working on tract number 740 of 1765 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 748 3.0 3 90.0 3.0375 200445.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 748 4.0 3 90.0 3.0171 200445.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 748 5.0 3 90.0 2.9968 200445.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 748 6.0 3 90.0 2.9767 200445.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 748 7.0 3 90.0 2.9567 200445.8\n",
      "I am working on tract number 760 of 1765 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 770 1 281488.7227192072 4.2919\n",
      "we have 2 non-opposing shorted wedges for tract no 774\n",
      "7 yoyos for tract,wedge,wedgePop,r= 774 3 370235.24000405485 2.904\n",
      "8 yoyos for tract,wedge,wedgePop,r= 774 3 67397.1518019695 1.452\n",
      "9 yoyos for tract,wedge,wedgePop,r= 774 3 867032.4633490975 3.0452\n",
      "10 yoyos for tract,wedge,wedgePop,r= 774 3 158909.14753941982 2.2486\n",
      "10 yoyos for tract,wedge,wedgePop,r= 774 3 210040.5331008119 2.6469\n",
      "10 yoyos for tract,wedge,wedgePop,r= 774 3 304280.3503691279 2.846\n",
      "11 yoyos for tract,wedge,wedgePop,r= 774 3 447821.6148978155 2.9456\n",
      "we have 2 non-opposing shorted wedges for tract no 778\n",
      "we have 2 non-opposing shorted wedges for tract no 779\n",
      "I am working on tract number 780 of 1765 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 780\n",
      "we have 2 non-opposing shorted wedges for tract no 782\n",
      "we have 2 non-opposing shorted wedges for tract no 783\n",
      "7 yoyos for tract,wedge,wedgePop,r= 785 2 911028.8258093362 2.9347\n",
      "8 yoyos for tract,wedge,wedgePop,r= 785 2 29986.080769956694 1.4673\n",
      "9 yoyos for tract,wedge,wedgePop,r= 785 2 978819.4435295827 3.2305\n",
      "9 yoyos for tract,wedge,wedgePop,r= 785 2 859273.6730610891 2.3489\n",
      "9 yoyos for tract,wedge,wedgePop,r= 785 2 550837.7569769593 1.9081\n",
      "10 yoyos for tract,wedge,wedgePop,r= 785 2 141515.32134203194 1.6877\n",
      "11 yoyos for tract,wedge,wedgePop,r= 785 2 320643.2575374112 1.7979\n",
      "12 yoyos for tract,wedge,wedgePop,r= 785 2 218042.39708163711 1.7428\n",
      "13 yoyos for tract,wedge,wedgePop,r= 785 2 271329.7179858421 1.7704\n",
      "13 yoyos for tract,wedge,wedgePop,r= 785 2 243273.23893696492 1.7566\n",
      "14 yoyos for tract,wedge,wedgePop,r= 785 2 229559.26413124654 1.7497\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 787 4.0 1 90.0 4.4758 265291.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 794 6.0 2 90.0 3.8371 281783.0\n",
      "7 yoyos for tract,wedge,wedgePop,r= 794 2 281782.95766280754 5.1825\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 796 5.0 1 90.0 4.4112 229299.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 796 6.0 1 90.0 3.9532 229299.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 796 15.0 1 90.0 5.1604 229299.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 796 16.0 1 90.0 4.6245 229299.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 796 17.0 1 90.0 4.1444 229299.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 796 18.0 1 90.0 3.714 229299.8\n",
      "7 yoyos for tract,wedge,wedgePop,r= 796 1 227952.85811890705 3.5724\n",
      "8 yoyos for tract,wedge,wedgePop,r= 796 1 29431.053606041038 1.7862\n",
      "9 yoyos for tract,wedge,wedgePop,r= 796 1 229299.7659932862 4.5049\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 799 5.0 2 90.0 3.5071 272826.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 799 13.0 2 90.0 4.1361 272826.7\n",
      "I am working on tract number 800 of 1765 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 800 5.0 2 90.0 4.0337 228703.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 800 6.0 2 90.0 3.6222 228703.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 800 11.0 2 90.0 4.1644 228703.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 800 12.0 2 90.0 3.7396 228703.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 800 17.0 2 90.0 3.9307 228703.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 800 18.0 2 90.0 3.5297 228703.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 809 6.0 2 90.0 3.6777 251348.5\n",
      "I am working on tract number 820 of 1765 tracts\n",
      "I am working on tract number 840 of 1765 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 853 5.0 1 90.0 3.9959 202664.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 853 6.0 1 90.0 3.9363 202664.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 853 7.0 1 90.0 3.8776 202664.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 853 8.0 1 90.0 3.8197 202664.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 853 9.0 1 90.0 3.7627 202664.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 853 10.0 1 90.0 3.7066 202664.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 853 15.0 1 90.0 4.9942 202664.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 853 16.0 1 90.0 4.9197 202664.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 853 17.0 1 90.0 4.8463 202664.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 853 18.0 1 90.0 4.774 202664.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 853 19.0 1 90.0 4.7027 202664.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 853 20.0 1 90.0 4.6326 202664.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 853 21.0 1 90.0 4.5635 202664.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 853 22.0 1 90.0 4.4954 202664.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 853 23.0 1 90.0 4.4283 202664.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 853 24.0 1 90.0 4.3622 202664.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 853 25.0 1 90.0 4.2971 202664.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 853 26.0 1 90.0 4.233 202664.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 853 27.0 1 90.0 4.1699 202664.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 853 28.0 1 90.0 4.1077 202664.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 853 29.0 1 90.0 4.0464 202664.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 853 30.0 1 90.0 3.986 202664.4\n",
      "loop31.0, tr853,wedgePops798774.67, 198549.9, 202664.4, 198801.1, 198759.2, Overedge?0, 0, 0, 0, ,Satisfied?1011,yoyo?2311 \n",
      "   targetWP, latest drx4 are tWP,dr, 198652.8,0.001, 198652.8,-0.0595, 198652.8,-0.0002, 198652.8,-0.0157\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 853 31.0 1 90.0 3.9265 202664.4\n",
      "loop32.0, tr853,wedgePops798774.67, 198549.9, 202664.4, 198801.1, 198759.2, Overedge?0, 0, 0, 0, ,Satisfied?1011,yoyo?2311 \n",
      "   targetWP, latest drx4 are tWP,dr, 198652.8,0.001, 198652.8,-0.0586, 198652.8,-0.0002, 198652.8,-0.0157\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 853 32.0 1 90.0 3.8679 202664.4\n",
      "loop33.0, tr853,wedgePops798774.67, 198549.9, 202664.4, 198801.1, 198759.2, Overedge?0, 0, 0, 0, ,Satisfied?1011,yoyo?2311 \n",
      "   targetWP, latest drx4 are tWP,dr, 198652.8,0.001, 198652.8,-0.0577, 198652.8,-0.0002, 198652.8,-0.0157\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 853 33.0 1 90.0 3.8102 202664.4\n",
      "loop34.0, tr853,wedgePops798774.67, 198549.9, 202664.4, 198801.1, 198759.2, Overedge?0, 0, 0, 0, ,Satisfied?1011,yoyo?2311 \n",
      "   targetWP, latest drx4 are tWP,dr, 198652.8,0.001, 198652.8,-0.0568, 198652.8,-0.0002, 198652.8,-0.0157\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 853 34.0 1 90.0 3.7534 202664.4\n",
      "loop35.0, tr853,wedgePops798774.67, 198549.9, 202664.4, 198801.1, 198759.2, Overedge?0, 0, 0, 0, ,Satisfied?1011,yoyo?2311 \n",
      "   targetWP, latest drx4 are tWP,dr, 198652.8,0.001, 198652.8,-0.056, 198652.8,-0.0002, 198652.8,-0.0157\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 853 35.0 1 90.0 3.6974 202664.4\n",
      "loop36.0, tr853,wedgePops798719.3, 198549.9, 202609.0, 198801.1, 198759.2, Overedge?0, 0, 0, 0, ,Satisfied?1011,yoyo?2311 \n",
      "   targetWP, latest drx4 are tWP,dr, 198652.8,0.001, 198652.8,-0.0552, 198652.8,-0.0002, 198652.8,-0.0157\n",
      "loop37.0, tr853,wedgePops627198.22, 198549.9, 31088.0, 198801.1, 198759.2, Overedge?0, 0, 0, 0, ,Satisfied?1011,yoyo?2311 \n",
      "   targetWP, latest drx4 are tWP,dr, 198652.8,0.001, 198652.8,-1.8211, 198652.8,-0.0002, 198652.8,-0.0157\n",
      "loop38.0, tr853,wedgePops660030.64, 198549.9, 63920.4, 198801.1, 198759.2, Overedge?0, 0, 0, 0, ,Satisfied?1011,yoyo?2411 \n",
      "   targetWP, latest drx4 are tWP,dr, 198652.8,0.001, 198652.8,1.4316, 198652.8,-0.0002, 198652.8,-0.0157\n",
      "loop39.0, tr853,wedgePops798774.67, 198549.9, 202664.4, 198801.1, 198759.2, Overedge?0, 0, 0, 0, ,Satisfied?1011,yoyo?2411 \n",
      "   targetWP, latest drx4 are tWP,dr, 198652.8,0.001, 198652.8,2.4819, 198652.8,-0.0002, 198652.8,-0.0157\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 0 1.22386 198121.9756 198549.93 0\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 1 5.73458 63920.3777 202664.4058 0\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 2 0.2712 199323.7766 198801.1166 0\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 3 1.444 199455.7911 198759.2168 0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loop40.0, tr853,wedgePops798774.67, 198549.9, 202664.4, 198801.1, 198759.2, Overedge?0, 0, 0, 0, ,Satisfied?1011,yoyo?2511 \n",
      "   targetWP, latest drx4 are tWP,dr, 198652.8,0.001, 198652.8,-0.0452, 198652.8,-0.0002, 198652.8,-0.0157\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 0 1.22386 198121.9756 198549.93 0\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 1 5.68938 202664.4058 202664.4058 0\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 2 0.2712 199323.7766 198801.1166 0\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 3 1.444 199455.7911 198759.2168 0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I looped 40 times on tract 853, giving up w pop 798774.6691690267\n",
      "I am working on tract number 860 of 1765 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 864\n",
      "we have 2 non-opposing shorted wedges for tract no 869\n",
      "we have 2 non-opposing shorted wedges for tract no 875\n",
      "we have 2 non-opposing shorted wedges for tract no 877\n",
      "I am working on tract number 880 of 1765 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 885 1 73153.57709695632 0.9226\n",
      "8 yoyos for tract,wedge,wedgePop,r= 885 1 1641541.663899165 4.3971\n",
      "8 yoyos for tract,wedge,wedgePop,r= 885 1 1439342.1640558417 2.6599\n",
      "8 yoyos for tract,wedge,wedgePop,r= 885 1 1403829.3922747585 1.7913\n",
      "8 yoyos for tract,wedge,wedgePop,r= 885 1 1221994.1175321825 1.3569\n",
      "8 yoyos for tract,wedge,wedgePop,r= 885 1 670656.0228420421 1.1398\n",
      "8 yoyos for tract,wedge,wedgePop,r= 885 1 251513.7966464298 1.0312\n",
      "9 yoyos for tract,wedge,wedgePop,r= 885 1 145501.44322058663 0.9769\n",
      "loop31.0, tr885,wedgePops771580.98, 186873.8, 187330.0, 187133.5, 210243.6, Overedge?1, 0, 0, 0, ,Satisfied?0011,yoyo?0902 \n",
      "   targetWP, latest drx4 are tWP,dr, 210431.7,0.8144, 210431.7,0.0271, 186873.8,-0.0006, 210431.7,0.0035\n",
      "9 yoyos for tract,wedge,wedgePop,r= 885 1 187330.03239550337 1.0041\n",
      "loop32.0, tr885,wedgePops800989.2, 186873.8, 216738.3, 187133.5, 210243.6, Overedge?1, 0, 0, 0, ,Satisfied?0011,yoyo?0902 \n",
      "   targetWP, latest drx4 are tWP,dr, 210431.7,0.8144, 210431.7,0.0136, 186873.8,-0.0006, 210431.7,0.0035\n",
      "10 yoyos for tract,wedge,wedgePop,r= 885 1 216738.25354321458 1.0176\n",
      "loop33.0, tr885,wedgePops785166.05, 186873.8, 200915.1, 187133.5, 210243.6, Overedge?1, 0, 0, 0, ,Satisfied?0011,yoyo?01002 \n",
      "   targetWP, latest drx4 are tWP,dr, 210431.7,0.8144, 210431.7,-0.0068, 186873.8,-0.0006, 210431.7,0.0035\n",
      "11 yoyos for tract,wedge,wedgePop,r= 885 1 200915.10479668906 1.0109\n",
      "loop34.0, tr885,wedgePops792832.92, 186873.8, 208582.0, 187133.5, 210243.6, Overedge?1, 0, 0, 0, ,Satisfied?0011,yoyo?01102 \n",
      "   targetWP, latest drx4 are tWP,dr, 210431.7,0.8144, 210431.7,0.0034, 186873.8,-0.0006, 210431.7,0.0035\n",
      "7 yoyos for tract,wedge,wedgePop,r= 893 1 238220.31354332864 1.023\n",
      "8 yoyos for tract,wedge,wedgePop,r= 893 1 30683.36598126561 0.5115\n",
      "9 yoyos for tract,wedge,wedgePop,r= 893 1 1616658.1225268936 1.4476\n",
      "10 yoyos for tract,wedge,wedgePop,r= 893 1 139305.6489847505 0.9795\n",
      "11 yoyos for tract,wedge,wedgePop,r= 893 1 1074400.4263279238 1.2135\n",
      "11 yoyos for tract,wedge,wedgePop,r= 893 1 478480.26929692016 1.0965\n",
      "11 yoyos for tract,wedge,wedgePop,r= 893 1 279664.5940947612 1.038\n",
      "I am working on tract number 900 of 1765 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 901 1 1863878.8708798995 2.8586\n",
      "7 yoyos for tract,wedge,wedgePop,r= 901 1 1638052.0855542826 1.4293\n",
      "8 yoyos for tract,wedge,wedgePop,r= 901 1 34455.06077325344 0.7147\n",
      "9 yoyos for tract,wedge,wedgePop,r= 901 1 1213209.7578727724 1.2296\n",
      "10 yoyos for tract,wedge,wedgePop,r= 901 1 150342.9507486741 0.9721\n",
      "11 yoyos for tract,wedge,wedgePop,r= 901 1 587151.0355750168 1.1008\n",
      "11 yoyos for tract,wedge,wedgePop,r= 901 1 322457.8737312883 1.0365\n",
      "11 yoyos for tract,wedge,wedgePop,r= 901 1 228320.1348692017 1.0043\n",
      "12 yoyos for tract,wedge,wedgePop,r= 901 1 187748.8139908544 0.9882\n",
      "loop31.0, tr901,wedgePops803721.61, 198556.9, 207922.1, 198349.8, 198892.9, Overedge?0, 0, 0, 0, ,Satisfied?1011,yoyo?61242 \n",
      "   targetWP, latest drx4 are tWP,dr, 198652.8,0.001, 198652.8,0.008, 198652.8,0.0096, 198652.8,0.0123\n",
      "13 yoyos for tract,wedge,wedgePop,r= 901 1 207922.06544335344 0.9962\n",
      "loop32.0, tr901,wedgePops793535.47, 198556.9, 197735.9, 198349.8, 198892.9, Overedge?0, 0, 0, 0, ,Satisfied?1011,yoyo?61342 \n",
      "   targetWP, latest drx4 are tWP,dr, 198652.8,0.001, 198652.8,-0.004, 198652.8,0.0096, 198652.8,0.0123\n",
      "7 yoyos for tract,wedge,wedgePop,r= 907 0 301199.9478178815 3.942\n",
      "8 yoyos for tract,wedge,wedgePop,r= 907 0 141744.62617194603 1.971\n",
      "9 yoyos for tract,wedge,wedgePop,r= 907 0 298164.773935852 3.7923\n",
      "9 yoyos for tract,wedge,wedgePop,r= 907 0 254677.40329566202 2.8816\n",
      "9 yoyos for tract,wedge,wedgePop,r= 907 0 216120.36122052284 2.4263\n",
      "10 yoyos for tract,wedge,wedgePop,r= 907 0 151826.14225315693 2.1986\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 909 10.0 0 90.0 3.2599 200552.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 912 4.0 3 90.0 3.1296 204288.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 912 5.0 3 90.0 3.0644 204288.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 912 6.0 3 90.0 3.0006 204288.1\n",
      "7 yoyos for tract,wedge,wedgePop,r= 912 0 460617.1515488476 1.156\n",
      "8 yoyos for tract,wedge,wedgePop,r= 912 0 16941.379476466274 0.578\n",
      "9 yoyos for tract,wedge,wedgePop,r= 912 0 3865309.0436947905 1.7182\n",
      "9 yoyos for tract,wedge,wedgePop,r= 912 0 412586.4093202492 1.1481\n",
      "10 yoyos for tract,wedge,wedgePop,r= 912 0 31429.535994067206 0.8631\n",
      "10 yoyos for tract,wedge,wedgePop,r= 912 0 102883.54741064752 1.0056\n",
      "10 yoyos for tract,wedge,wedgePop,r= 912 0 171895.45001089276 1.0769\n",
      "11 yoyos for tract,wedge,wedgePop,r= 912 0 255556.83582000405 1.1125\n",
      "11 yoyos for tract,wedge,wedgePop,r= 912 0 209098.04463932605 1.0947\n",
      "12 yoyos for tract,wedge,wedgePop,r= 912 0 189234.08224785706 1.0858\n",
      "I am working on tract number 920 of 1765 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 931 1 20783.84886165103 0.7472\n",
      "8 yoyos for tract,wedge,wedgePop,r= 931 1 1613646.8514606806 3.4065\n",
      "8 yoyos for tract,wedge,wedgePop,r= 931 1 1545811.0743702124 2.0768\n",
      "8 yoyos for tract,wedge,wedgePop,r= 931 1 1360992.6641000472 1.412\n",
      "8 yoyos for tract,wedge,wedgePop,r= 931 1 362981.25846972305 1.0796\n",
      "9 yoyos for tract,wedge,wedgePop,r= 931 1 50806.821949503676 0.9134\n",
      "9 yoyos for tract,wedge,wedgePop,r= 931 1 157111.31626299967 0.9965\n",
      "10 yoyos for tract,wedge,wedgePop,r= 931 1 234995.48789140666 1.0381\n",
      "loop31.0, tr931,wedgePops772834.76, 181339.0, 194843.1, 180986.8, 215665.9, Overedge?1, 0, 0, 0, ,Satisfied?0011,yoyo?01030 \n",
      "   targetWP, latest drx4 are tWP,dr, 215966.6,0.7099, 215966.6,-0.0208, 181339.0,0.0013, 215966.6,0.0358\n",
      "11 yoyos for tract,wedge,wedgePop,r= 931 1 194843.0752000552 1.0173\n",
      "loop32.0, tr931,wedgePops791002.1, 181339.0, 213010.4, 180986.8, 215665.9, Overedge?1, 0, 0, 0, ,Satisfied?0011,yoyo?01130 \n",
      "   targetWP, latest drx4 are tWP,dr, 215966.6,0.7099, 215966.6,0.0104, 181339.0,0.0013, 215966.6,0.0358\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 935 13.0 2 90.0 3.8173 282768.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 935 14.0 2 90.0 2.8907 282768.3\n",
      "7 yoyos for tract,wedge,wedgePop,r= 935 2 282768.2886320927 3.2729\n",
      "7 yoyos for tract,wedge,wedgePop,r= 936 3 316927.5155316823 1.8149\n",
      "8 yoyos for tract,wedge,wedgePop,r= 936 3 96548.55794989993 0.9074\n",
      "9 yoyos for tract,wedge,wedgePop,r= 936 3 415740.2285626434 2.073\n",
      "9 yoyos for tract,wedge,wedgePop,r= 936 3 241224.89657985928 1.4902\n",
      "10 yoyos for tract,wedge,wedgePop,r= 936 3 107660.78736588373 1.1988\n",
      "10 yoyos for tract,wedge,wedgePop,r= 936 3 126613.31385922569 1.3445\n",
      "10 yoyos for tract,wedge,wedgePop,r= 936 3 156789.98309383076 1.4174\n",
      "10 yoyos for tract,wedge,wedgePop,r= 936 3 194612.04568630093 1.4538\n",
      "11 yoyos for tract,wedge,wedgePop,r= 936 3 220040.20113154402 1.472\n",
      "11 yoyos for tract,wedge,wedgePop,r= 936 3 207857.40798087494 1.4629\n",
      "I am working on tract number 940 of 1765 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 948 6.0 0 90.0 3.3017 281636.5\n",
      "7 yoyos for tract,wedge,wedgePop,r= 948 0 279928.7732541891 3.1035\n",
      "8 yoyos for tract,wedge,wedgePop,r= 948 0 92116.95149162586 1.5518\n",
      "9 yoyos for tract,wedge,wedgePop,r= 948 0 281120.3813089701 3.1353\n",
      "10 yoyos for tract,wedge,wedgePop,r= 948 0 104462.96663534932 2.3435\n",
      "10 yoyos for tract,wedge,wedgePop,r= 948 0 126055.43396548176 2.7394\n",
      "10 yoyos for tract,wedge,wedgePop,r= 948 0 149119.22907885496 2.9374\n",
      "10 yoyos for tract,wedge,wedgePop,r= 948 0 171846.3205732125 3.0363\n",
      "11 yoyos for tract,wedge,wedgePop,r= 948 0 266148.7730428111 3.0858\n",
      "11 yoyos for tract,wedge,wedgePop,r= 948 0 203458.0692176583 3.0611\n",
      "12 yoyos for tract,wedge,wedgePop,r= 948 0 182170.17626218687 3.0487\n",
      "12 yoyos for tract,wedge,wedgePop,r= 948 0 190867.98537425997 3.0549\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 951 9.0 1 90.0 3.6869 284970.3\n",
      "we have 2 non-opposing shorted wedges for tract no 954\n",
      "I am working on tract number 960 of 1765 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 978 8.0 0 90.0 3.0148 272243.9\n",
      "7 yoyos for tract,wedge,wedgePop,r= 978 0 272243.90223988524 4.6797\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 978 26.0 0 90.0 2.24 216135.7\n",
      "I am working on tract number 980 of 1765 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 990 2.0 2 90.0 0.2479 6982.2\n",
      "we have 2 non-opposing shorted wedges for tract no 990\n",
      "7 yoyos for tract,wedge,wedgePop,r= 992 2 1088193.8065879762 5.5107\n",
      "8 yoyos for tract,wedge,wedgePop,r= 992 2 79356.10456695664 2.7554\n",
      "9 yoyos for tract,wedge,wedgePop,r= 992 2 1346298.7794595712 6.633\n",
      "10 yoyos for tract,wedge,wedgePop,r= 992 2 249985.36998309405 4.6942\n",
      "11 yoyos for tract,wedge,wedgePop,r= 992 2 1178493.9770377858 5.6636\n",
      "11 yoyos for tract,wedge,wedgePop,r= 992 2 636945.6338211333 5.1789\n",
      "12 yoyos for tract,wedge,wedgePop,r= 992 2 426174.7139120881 4.9365\n",
      "12 yoyos for tract,wedge,wedgePop,r= 992 2 520167.5036241846 5.0577\n",
      "12 yoyos for tract,wedge,wedgePop,r= 992 2 576631.3113411695 5.1183\n",
      "13 yoyos for tract,wedge,wedgePop,r= 992 2 606356.2007101171 5.1486\n",
      "14 yoyos for tract,wedge,wedgePop,r= 992 2 591447.4229127548 5.1335\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 998 6.0 0 90.0 3.2067 271125.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 998 11.0 0 90.0 3.2127 271125.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 998 16.0 0 90.0 3.6245 271125.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 998 17.0 0 90.0 2.8415 271125.2\n",
      "7 yoyos for tract,wedge,wedgePop,r= 998 0 247315.338668247 2.5018\n",
      "8 yoyos for tract,wedge,wedgePop,r= 998 0 89717.5776762481 1.2509\n",
      "9 yoyos for tract,wedge,wedgePop,r= 998 0 271125.2218219926 3.3792\n",
      "I am working on tract number 1000 of 1765 tracts\n",
      "I am working on tract number 1020 of 1765 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1021\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1021 0 1135615.4692258574 3.572\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1021 0 52960.77166446182 1.786\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1021 0 1328202.1712929136 4.0861\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1021 0 1026867.1899865678 2.9361\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1021 0 313292.45498263976 2.361\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1021 0 882678.2224315736 2.6486\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1021 0 629609.5292218693 2.5048\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1021 0 470762.6794711121 2.4329\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1021 0 399523.61519221927 2.397\n",
      "12 yoyos for tract,wedge,wedgePop,r= 1021 0 357502.4143286341 2.379\n",
      "13 yoyos for tract,wedge,wedgePop,r= 1021 0 379125.05901402066 2.388\n",
      "14 yoyos for tract,wedge,wedgePop,r= 1021 0 368458.99065721827 2.3835\n",
      "I am working on tract number 1040 of 1765 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1052 3 289909.86173921847 3.469\n",
      "I am working on tract number 1060 of 1765 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1077 9.0 1 90.0 2.7127 202482.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1077 10.0 1 90.0 2.6741 202482.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1077 11.0 1 90.0 2.636 202482.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1077 12.0 1 90.0 2.5984 202482.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1077 13.0 1 90.0 2.5613 202482.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1077 14.0 1 90.0 2.5248 202482.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1077 15.0 1 90.0 2.4888 202482.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1077 20.0 1 90.0 2.7312 202482.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1077 21.0 1 90.0 2.6922 202482.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1077 22.0 1 90.0 2.6539 202482.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1077 23.0 1 90.0 2.616 202482.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1077 24.0 1 90.0 2.5787 202482.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1077 25.0 1 90.0 2.542 202482.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1077 26.0 1 90.0 2.5057 202482.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1077 27.0 1 90.0 2.47 202482.9\n",
      "loop31.0, tr1077,wedgePops798855.78, 198935.1, 202482.9, 198400.0, 199037.8, Overedge?0, 0, 0, 0, ,Satisfied?1011,yoyo?1401 \n",
      "   targetWP, latest drx4 are tWP,dr, 198652.8,-0.0004, 198652.8,0.4398, 198652.8,0.0006, 198652.8,-0.0004\n",
      "loop32.0, tr1077,wedgePops798855.78, 198935.1, 202482.9, 198400.0, 199037.8, Overedge?0, 0, 0, 0, ,Satisfied?1011,yoyo?1501 \n",
      "   targetWP, latest drx4 are tWP,dr, 198652.8,-0.0004, 198652.8,-0.0749, 198652.8,0.0006, 198652.8,-0.0004\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1077 32.0 1 90.0 2.6373 202482.9\n",
      "loop33.0, tr1077,wedgePops798855.78, 198935.1, 202482.9, 198400.0, 199037.8, Overedge?0, 0, 0, 0, ,Satisfied?1011,yoyo?1501 \n",
      "   targetWP, latest drx4 are tWP,dr, 198652.8,-0.0004, 198652.8,-0.0376, 198652.8,0.0006, 198652.8,-0.0004\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1077 33.0 1 90.0 2.5997 202482.9\n",
      "loop34.0, tr1077,wedgePops798855.78, 198935.1, 202482.9, 198400.0, 199037.8, Overedge?0, 0, 0, 0, ,Satisfied?1011,yoyo?1501 \n",
      "   targetWP, latest drx4 are tWP,dr, 198652.8,-0.0004, 198652.8,-0.0371, 198652.8,0.0006, 198652.8,-0.0004\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1077 34.0 1 90.0 2.5627 202482.9\n",
      "loop35.0, tr1077,wedgePops798855.78, 198935.1, 202482.9, 198400.0, 199037.8, Overedge?0, 0, 0, 0, ,Satisfied?1011,yoyo?1501 \n",
      "   targetWP, latest drx4 are tWP,dr, 198652.8,-0.0004, 198652.8,-0.0365, 198652.8,0.0006, 198652.8,-0.0004\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1077 35.0 1 90.0 2.5261 202482.9\n",
      "loop36.0, tr1077,wedgePops798855.78, 198935.1, 202482.9, 198400.0, 199037.8, Overedge?0, 0, 0, 0, ,Satisfied?1011,yoyo?1501 \n",
      "   targetWP, latest drx4 are tWP,dr, 198652.8,-0.0004, 198652.8,-0.036, 198652.8,0.0006, 198652.8,-0.0004\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1077 36.0 1 90.0 2.4901 202482.9\n",
      "loop37.0, tr1077,wedgePops798815.46, 198935.1, 202442.6, 198400.0, 199037.8, Overedge?0, 0, 0, 0, ,Satisfied?1011,yoyo?1501 \n",
      "   targetWP, latest drx4 are tWP,dr, 198652.8,-0.0004, 198652.8,-0.0355, 198652.8,0.0006, 198652.8,-0.0004\n",
      "loop38.0, tr1077,wedgePops767249.56, 198935.1, 170876.7, 198400.0, 199037.8, Overedge?0, 0, 0, 0, ,Satisfied?1011,yoyo?1501 \n",
      "   targetWP, latest drx4 are tWP,dr, 198652.8,-0.0004, 198652.8,-1.2273, 198652.8,0.0006, 198652.8,-0.0004\n",
      "loop39.0, tr1077,wedgePops778287.74, 198935.1, 181914.9, 198400.0, 199037.8, Overedge?0, 0, 0, 0, ,Satisfied?1011,yoyo?1601 \n",
      "   targetWP, latest drx4 are tWP,dr, 198652.8,-0.0004, 198652.8,0.8914, 198652.8,0.0006, 198652.8,-0.0004\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 0 0.1563 200078.7465 198935.0575 0\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 1 2.11867 170876.7166 181914.8944 0\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 2 0.17401 197495.4076 198400.0184 0\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 3 0.12769 200661.7334 199037.7681 0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loop40.0, tr1077,wedgePops798855.78, 198935.1, 202482.9, 198400.0, 199037.8, Overedge?0, 0, 0, 0, ,Satisfied?1011,yoyo?1601 \n",
      "   targetWP, latest drx4 are tWP,dr, 198652.8,-0.0004, 198652.8,0.7066, 198652.8,0.0006, 198652.8,-0.0004\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 0 0.1563 200078.7465 198935.0575 0\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 1 2.82523 181914.8944 202482.9391 0\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 2 0.17401 197495.4076 198400.0184 0\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 3 0.12769 200661.7334 199037.7681 0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I looped 40 times on tract 1077, giving up w pop 798855.7830161117\n",
      "I am working on tract number 1080 of 1765 tracts\n",
      "I am working on tract number 1100 of 1765 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1107 13.0 2 90.0 3.4576 253586.6\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1107 2 253586.58818494034 3.7925\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1110 5.0 3 90.0 3.2611 287140.4\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1111 2 247180.08501511492 0.9567\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1111 2 168372.67630922754 0.4784\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1111 2 248751.6133485085 0.9657\n",
      "I am working on tract number 1120 of 1765 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1129 9.0 0 90.0 3.5512 254216.1\n",
      "I am working on tract number 1140 of 1765 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1148 9.0 1 90.0 3.6173 232603.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1148 10.0 1 90.0 3.2057 232603.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1148 11.0 1 90.0 2.8409 232603.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1148 12.0 1 90.0 2.5177 232603.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1157 5.0 1 90.0 3.4896 243715.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1157 10.0 1 90.0 3.7903 243715.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1157 11.0 1 90.0 3.2379 243715.3\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1157 1 243715.30470231376 3.3578\n",
      "I am working on tract number 1160 of 1765 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1169 3 393387.1950100642 2.6631\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1169 3 137706.74947974854 1.3315\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1169 3 385559.35141466884 2.5886\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1169 3 188463.00066374562 1.9601\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1169 3 230944.40185560405 2.2743\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1169 3 349073.097420816 2.4315\n",
      "12 yoyos for tract,wedge,wedgePop,r= 1169 3 251035.2592714383 2.3529\n",
      "12 yoyos for tract,wedge,wedgePop,r= 1169 3 300229.299168003 2.3922\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1170 6.0 3 90.0 3.1482 254005.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1176 5.0 2 90.0 2.7849 276443.8\n",
      "I am working on tract number 1180 of 1765 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1188 1 216533.95214989566 0.5079\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1188 1 153805.99821187713 0.2539\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1188 1 227758.91597923636 0.6442\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1188 1 178045.51466003284 0.4491\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1188 1 221967.64790714928 0.5467\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1188 1 211317.82158100186 0.4979\n",
      "I am working on tract number 1200 of 1765 tracts\n",
      "I am working on tract number 1220 of 1765 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1223 0 215171.9783498545 0.3146\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1223 0 94986.4054431418 0.1573\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1223 0 255175.16473869106 0.3788\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1223 0 164768.7763692914 0.268\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1223 0 225657.13045350375 0.3234\n",
      "12 yoyos for tract,wedge,wedgePop,r= 1223 0 188869.5559837732 0.2957\n",
      "13 yoyos for tract,wedge,wedgePop,r= 1223 0 208825.58549861365 0.3096\n",
      "we have 2 non-opposing shorted wedges for tract no 1226\n",
      "we have 2 non-opposing shorted wedges for tract no 1229\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1232 1 768150.3877072588 5.7094\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1232 1 240959.99593739037 2.8547\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1232 1 769610.1651769681 5.7466\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1232 1 421063.52012011973 4.3006\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1232 1 541933.8412171279 5.0236\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1232 1 611996.2519234626 5.3851\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1232 1 729869.6167368862 5.5658\n",
      "12 yoyos for tract,wedge,wedgePop,r= 1232 1 632999.2885712199 5.4755\n",
      "12 yoyos for tract,wedge,wedgePop,r= 1232 1 660312.5177929335 5.5206\n",
      "12 yoyos for tract,wedge,wedgePop,r= 1232 1 701923.2223525612 5.5432\n",
      "13 yoyos for tract,wedge,wedgePop,r= 1232 1 716694.9575744432 5.5545\n",
      "I am working on tract number 1240 of 1765 tracts\n",
      "I am working on tract number 1260 of 1765 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1261 2 254078.88926094508 0.4966\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1261 2 185768.97540845067 0.2483\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1261 2 254073.98419198891 0.4966\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1261 2 224255.93739645908 0.3725\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1261 2 187536.11719891863 0.3104\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1269 1 208230.20046548801 1.3172\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1269 1 152328.99158537138 0.6586\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1269 1 231348.12175494587 1.3957\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1269 1 160008.50668896025 1.0272\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1269 1 170817.47216570404 1.2114\n",
      "I am working on tract number 1280 of 1765 tracts\n",
      "I am working on tract number 1300 of 1765 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1303 5.0 0 90.0 4.4644 263829.4\n",
      "I am working on tract number 1320 of 1765 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1333 4.0 0 90.0 5.0506 221764.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1333 5.0 0 90.0 4.645 221764.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1333 6.0 0 90.0 4.272 221764.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1333 11.0 0 90.0 5.0036 221764.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1333 12.0 0 90.0 4.6018 221764.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1333 13.0 0 90.0 4.2322 221764.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1333 18.0 0 90.0 5.4828 221764.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1333 19.0 0 90.0 5.0424 221764.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1333 20.0 0 90.0 4.6375 221764.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1333 21.0 0 90.0 4.265 221764.1\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1333 0 221764.14363175738 5.4788\n",
      "loop31.0, tr1333,wedgePops859209.82, 61286.4, 391256.9, 16977.3, 389689.3, Overedge?1, 0, 0, 0, ,Satisfied?0000,yoyo?0323 \n",
      "   targetWP, latest drx4 are tWP,dr, 358158.9,1.5482, 358158.9,-0.0479, 17006.9,0.0009, 358158.9,-0.0715\n",
      "loop32.0, tr1333,wedgePops805784.5, 61286.4, 362733.9, 16977.3, 364786.9, Overedge?1, 0, 0, 0, ,Satisfied?0010,yoyo?0323 \n",
      "   targetWP, latest drx4 are tWP,dr, 358158.9,1.5482, 358158.9,-0.0095, 17006.9,0.0009, 358158.9,-0.0123\n",
      "loop33.0, tr1333,wedgePops796191.71, 61286.4, 358634.8, 16977.3, 359293.2, Overedge?1, 0, 0, 0, ,Satisfied?0010,yoyo?0323 \n",
      "   targetWP, latest drx4 are tWP,dr, 358158.9,1.5482, 358158.9,-0.0012, 17006.9,0.0009, 358158.9,-0.0026\n",
      "we have 2 non-opposing shorted wedges for tract no 1334\n",
      "I am working on tract number 1340 of 1765 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1348 3.0 3 90.0 2.6376 214216.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1348 8.0 3 90.0 3.4413 214216.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1348 9.0 3 90.0 3.2502 214216.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1348 10.0 3 90.0 3.0698 214216.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1348 11.0 3 90.0 2.8994 214216.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1348 12.0 3 90.0 2.7384 214216.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1348 13.0 3 90.0 2.5864 214216.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1348 18.0 3 90.0 3.7817 214216.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1348 19.0 3 90.0 3.5717 214216.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1348 20.0 3 90.0 3.3734 214216.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1348 21.0 3 90.0 3.1861 214216.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1348 22.0 3 90.0 3.0092 214216.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1348 23.0 3 90.0 2.8422 214216.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1348 24.0 3 90.0 2.6844 214216.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1348 25.0 3 90.0 2.5353 214216.6\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1348 3 214086.6862921591 2.4733\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1348 3 27556.90924274031 1.2367\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1348 3 214216.55203459098 3.1826\n",
      "loop31.0, tr1348,wedgePops2806550.44, 49699.6, 2707464.5, 27434.1, 21952.3, Overedge?0, 0, 0, 0, ,Satisfied?0000,yoyo?0000 \n",
      "   targetWP, latest drx4 are tWP,dr, 198652.8,1.1191, 198652.8,1.1191, 198652.8,1.1191, 198652.8,1.1191\n",
      "loop32.0, tr1348,wedgePops1042648.92, 100710.4, 377886.5, 395291.7, 168760.3, Overedge?0, 0, 0, 0, ,Satisfied?0000,yoyo?0100 \n",
      "   targetWP, latest drx4 are tWP,dr, 198652.8,0.8989, 198652.8,-0.5622, 198652.8,1.5211, 198652.8,1.7676\n",
      "loop33.0, tr1348,wedgePops902973.2, 233130.1, 245606.8, 255405.8, 168830.4, Overedge?0, 0, 0, 1, ,Satisfied?0000,yoyo?0000 \n",
      "   targetWP, latest drx4 are tWP,dr, 208593.6,0.8505, 208593.6,-0.0553, 208593.6,-0.5298, 208593.6,0.192\n",
      "loop34.0, tr1348,wedgePops861249.39, 233130.1, 213678.9, 245610.0, 168830.4, Overedge?0, 0, 0, 1, ,Satisfied?0000,yoyo?1000 \n",
      "   targetWP, latest drx4 are tWP,dr, 208593.6,-0.1101, 208593.6,-0.0133, 208593.6,-0.1679, 208593.6,0.192\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1348 34.0 0 90.0 2.7637 233130.1\n",
      "loop35.0, tr1348,wedgePops668137.78, 233130.1, 209627.4, 56549.8, 168830.4, Overedge?0, 0, 0, 1, ,Satisfied?0000,yoyo?1000 \n",
      "   targetWP, latest drx4 are tWP,dr, 208593.6,-0.2242, 208593.6,-0.0017, 208593.6,-0.6763, 208593.6,0.192\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1348 35.0 0 90.0 2.5395 233130.1\n",
      "loop36.0, tr1348,wedgePops828289.11, 231546.7, 208811.8, 219100.2, 168830.4, Overedge?0, 0, 0, 1, ,Satisfied?0000,yoyo?1010 \n",
      "   targetWP, latest drx4 are tWP,dr, 208593.6,-0.206, 208593.6,-0.0004, 208593.6,0.4509, 208593.6,0.192\n",
      "loop37.0, tr1348,wedgePops638019.55, 50511.0, 208811.8, 209866.3, 168830.4, Overedge?0, 0, 0, 1, ,Satisfied?0100,yoyo?1020 \n",
      "   targetWP, latest drx4 are tWP,dr, 208593.6,-1.1667, 208593.6,-0.0004, 208593.6,-0.0204, 208593.6,0.192\n",
      "loop38.0, tr1348,wedgePops684548.22, 98191.7, 208811.8, 208714.3, 168830.4, Overedge?0, 0, 0, 1, ,Satisfied?0100,yoyo?2020 \n",
      "   targetWP, latest drx4 are tWP,dr, 208593.6,0.8424, 208593.6,-0.0004, 208593.6,-0.0023, 208593.6,0.192\n",
      "loop39.0, tr1348,wedgePops819486.59, 233130.1, 208811.8, 208714.3, 168830.4, Overedge?0, 0, 0, 1, ,Satisfied?0110,yoyo?2020 \n",
      "   targetWP, latest drx4 are tWP,dr, 208593.6,0.9516, 208593.6,-0.0004, 208593.6,-0.0023, 208593.6,0.192\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 0 2.96076 98191.7064 233130.082 0\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 1 0.49154 209627.4378 208811.7925 0\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 2 1.69969 209866.3419 208714.2717 0\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 3 3.08397 168760.3434 168830.4483 1\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loop40.0, tr1348,wedgePops819486.59, 233130.1, 208811.8, 208714.3, 168830.4, Overedge?0, 0, 0, 1, ,Satisfied?0110,yoyo?3020 \n",
      "   targetWP, latest drx4 are tWP,dr, 208593.6,-0.1192, 208593.6,-0.0004, 208593.6,-0.0023, 208593.6,0.192\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 0 2.84158 233130.082 233130.082 0\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 1 0.49154 209627.4378 208811.7925 0\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 2 1.69969 209866.3419 208714.2717 0\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 3 3.08397 168760.3434 168830.4483 1\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I looped 40 times on tract 1348, giving up w pop 819486.5945401154\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1349 10.0 0 90.0 3.4119 229267.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1349 11.0 0 90.0 3.058 229267.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1349 12.0 0 90.0 2.7407 229267.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1349 13.0 0 90.0 2.4564 229267.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1349 18.0 0 90.0 3.4202 229267.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1349 19.0 0 90.0 3.0654 229267.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1349 20.0 0 90.0 2.7474 229267.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1349 21.0 0 90.0 2.4624 229267.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1349 26.0 0 90.0 3.6004 229267.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1349 27.0 0 90.0 3.2269 229267.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1349 28.0 0 90.0 2.8922 229267.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1349 29.0 0 90.0 2.5922 229267.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1349 30.0 0 90.0 2.3233 229267.2\n",
      "loop31.0, tr1349,wedgePops702006.74, 106701.5, 198465.5, 198379.9, 198459.8, Overedge?0, 0, 0, 0, ,Satisfied?0111,yoyo?5023 \n",
      "   targetWP, latest drx4 are tWP,dr, 198652.8,-0.241, 198652.8,0.0008, 198652.8,0.0008, 198652.8,-0.0107\n",
      "loop32.0, tr1349,wedgePops824492.12, 229186.9, 198465.5, 198379.9, 198459.8, Overedge?0, 0, 0, 0, ,Satisfied?0111,yoyo?6023 \n",
      "   targetWP, latest drx4 are tWP,dr, 198652.8,0.1466, 198652.8,0.0008, 198652.8,0.0008, 198652.8,-0.0107\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1349 0 229186.8860664166 2.2288\n",
      "loop33.0, tr1349,wedgePops646272.22, 50967.0, 198465.5, 198379.9, 198459.8, Overedge?0, 0, 0, 0, ,Satisfied?0111,yoyo?7023 \n",
      "   targetWP, latest drx4 are tWP,dr, 198652.8,-1.1144, 198652.8,0.0008, 198652.8,0.0008, 198652.8,-0.0107\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1349 0 50966.986188390845 1.1144\n",
      "loop34.0, tr1349,wedgePops824572.44, 229267.2, 198465.5, 198379.9, 198459.8, Overedge?0, 0, 0, 0, ,Satisfied?0111,yoyo?8023 \n",
      "   targetWP, latest drx4 are tWP,dr, 198652.8,1.9171, 198652.8,0.0008, 198652.8,0.0008, 198652.8,-0.0107\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1349 0 229267.1994419895 3.0315\n",
      "loop35.0, tr1349,wedgePops699864.55, 104559.3, 198465.5, 198379.9, 198459.8, Overedge?1, 0, 0, 0, ,Satisfied?0111,yoyo?0000 \n",
      "   targetWP, latest drx4 are tWP,dr, 230017.3,-0.9585, 230017.3,0.0008, 230017.3,0.0008, 230017.3,-0.0107\n",
      "loop36.0, tr1349,wedgePops772997.76, 104559.3, 221031.1, 221347.3, 226060.1, Overedge?1, 0, 0, 0, ,Satisfied?0000,yoyo?0001 \n",
      "   targetWP, latest drx4 are tWP,dr, 230017.3,-0.9585, 230017.3,0.0145, 230017.3,0.0084, 230017.3,0.0506\n",
      "loop37.0, tr1349,wedgePops788045.78, 104559.3, 226997.1, 227994.5, 228494.9, Overedge?1, 0, 0, 0, ,Satisfied?0000,yoyo?0001 \n",
      "   targetWP, latest drx4 are tWP,dr, 230017.3,-0.9585, 230017.3,0.0045, 230017.3,0.0025, 230017.3,0.0057\n",
      "loop38.0, tr1349,wedgePops793088.94, 104559.3, 229413.1, 229623.8, 229492.7, Overedge?1, 0, 0, 0, ,Satisfied?0000,yoyo?0001 \n",
      "   targetWP, latest drx4 are tWP,dr, 230017.3,-0.9585, 230017.3,0.0018, 230017.3,0.0006, 230017.3,0.0029\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1352 3.0 1 90.0 3.0866 215044.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1352 4.0 1 90.0 2.9066 215044.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1352 5.0 1 90.0 2.7372 215044.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1352 10.0 1 90.0 3.1984 215044.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1352 11.0 1 90.0 3.0119 215044.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1352 12.0 1 90.0 2.8363 215044.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1352 17.0 1 90.0 3.1867 215044.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1352 18.0 1 90.0 3.0009 215044.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1352 19.0 1 90.0 2.8259 215044.9\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1352 1 215044.87878104852 3.2649\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1355 3 381427.6446042342 1.8901\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1355 3 137202.71989772134 0.945\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1355 3 405188.5196990381 1.9117\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1355 3 295376.44964337733 1.4283\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1355 3 150106.700129213 1.1867\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1355 3 179264.22759879392 1.3075\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1355 3 238123.40158140624 1.3679\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1357 6.0 3 90.0 3.3996 240894.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1357 7.0 3 90.0 2.931 240894.6\n",
      "I am working on tract number 1360 of 1765 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1374 3.0 3 90.0 3.0824 239773.0\n",
      "I am working on tract number 1380 of 1765 tracts\n",
      "I am working on tract number 1400 of 1765 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1410 2 237815.2105267962 0.7358\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1410 2 166270.30452865383 0.3679\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1410 2 237802.85559186904 0.7355\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1410 2 228924.1187683418 0.5517\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1410 2 169757.74668912616 0.4598\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1417 10.0 2 90.0 3.1497 283399.7\n",
      "I am working on tract number 1420 of 1765 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1427 5.0 2 90.0 4.2215 219374.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1427 6.0 2 90.0 3.915 219374.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1427 7.0 2 90.0 3.6308 219374.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1427 8.0 2 90.0 3.3672 219374.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1427 13.0 2 90.0 4.1905 219374.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1427 14.0 2 90.0 3.8862 219374.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1427 15.0 2 90.0 3.6041 219374.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1427 16.0 2 90.0 3.3424 219374.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1427 21.0 2 90.0 3.4848 219374.7\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1427 2 219374.71887416107 4.1056\n",
      "loop31.0, tr1427,wedgePops772896.51, 24172.1, 372636.9, 23373.8, 352713.7, Overedge?1, 0, 0, 0, ,Satisfied?0100,yoyo?0102 \n",
      "   targetWP, latest drx4 are tWP,dr, 373297.4,1.7676, 373297.4,-0.0707, 23844.1,0.0028, 373297.4,0.1346\n",
      "loop32.0, tr1427,wedgePops784109.86, 24172.1, 372636.9, 23665.2, 363635.6, Overedge?1, 0, 0, 0, ,Satisfied?0100,yoyo?0102 \n",
      "   targetWP, latest drx4 are tWP,dr, 373297.4,1.7676, 373297.4,-0.0707, 23844.1,0.0016, 373297.4,0.1479\n",
      "loop33.0, tr1427,wedgePops786313.21, 24172.1, 372636.9, 23786.9, 365717.3, Overedge?1, 0, 0, 0, ,Satisfied?0100,yoyo?0102 \n",
      "   targetWP, latest drx4 are tWP,dr, 373297.4,1.7676, 373297.4,-0.0707, 23844.1,0.0008, 373297.4,0.0943\n",
      "loop34.0, tr1427,wedgePops791097.66, 24172.1, 372636.9, 23786.9, 370501.8, Overedge?1, 0, 0, 0, ,Satisfied?0110,yoyo?0102 \n",
      "   targetWP, latest drx4 are tWP,dr, 373297.4,1.7676, 373297.4,-0.0707, 23844.1,0.0008, 373297.4,0.2392\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1429 5.0 0 90.0 3.2846 217801.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1429 10.0 0 90.0 3.9473 217801.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1429 11.0 0 90.0 3.681 217801.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1429 12.0 0 90.0 3.4327 217801.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1429 13.0 0 90.0 3.2012 217801.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1429 18.0 0 90.0 4.1357 217801.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1429 19.0 0 90.0 3.8567 217801.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1429 20.0 0 90.0 3.5966 217801.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1429 21.0 0 90.0 3.354 217801.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1429 22.0 0 90.0 3.1277 217801.7\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1429 0 217719.34671787987 3.0342\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1429 0 29009.42931936076 1.5171\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1429 0 217801.73014397718 3.6551\n",
      "loop31.0, tr1429,wedgePops796532.71, 32106.5, 383085.6, 12079.3, 369261.2, Overedge?1, 0, 0, 0, ,Satisfied?0000,yoyo?0111 \n",
      "   targetWP, latest drx4 are tWP,dr, 365199.0,1.7676, 365199.0,-0.0289, 32106.5,-0.0837, 365199.0,-0.0126\n",
      "we have 2 non-opposing shorted wedges for tract no 1431\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1435 2 136254.06199035142 1.4664\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1435 2 6178975.666983047 5.0313\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1435 2 2768562.2172637377 3.2488\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1435 2 1018516.5936744518 2.3576\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1435 2 897637.6619505935 1.912\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1435 2 514472.99688006594 1.6892\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1435 2 297504.0027723268 1.5778\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1435 2 218414.0438055981 1.5221\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1437 2 186391.44049996475 2.6599\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1437 2 6464004.200273303 7.6797\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1437 2 3282724.417049044 5.1698\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1437 2 1276726.4776956555 3.9149\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1437 2 1101984.6279501347 3.2874\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1437 2 542247.759217837 2.9737\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1437 2 350625.2835711643 2.8168\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1437 2 441502.7658188592 2.8952\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1437 2 485729.0327431896 2.9345\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1437 2 513467.6560914675 2.9541\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1437 2 528056.7036641492 2.9639\n",
      "I am working on tract number 1440 of 1765 tracts\n",
      "I am working on tract number 1460 of 1765 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1460\n",
      "we have 2 non-opposing shorted wedges for tract no 1463\n",
      "we have 2 non-opposing shorted wedges for tract no 1478\n",
      "I am working on tract number 1480 of 1765 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1489\n",
      "we have 2 non-opposing shorted wedges for tract no 1497\n",
      "I am working on tract number 1500 of 1765 tracts\n",
      "I am working on tract number 1520 of 1765 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1528 3.0 1 36.8 1.1747 88707.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1529 5.0 1 90.0 0.1358 37663.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1529 6.0 1 90.0 0.4584 37663.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1531 2.0 2 90.0 0.3273 4014.7\n",
      "we have 2 non-opposing shorted wedges for tract no 1531\n",
      "I am working on tract number 1540 of 1765 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1541\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1541 3 966319.182422207 8.6281\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1541 3 807781.1931721087 4.3141\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1541 3 59979.40099299734 2.157\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1541 3 742360.4110009954 3.8913\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1541 3 63737.99163046107 3.0241\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1541 3 95697.03113729668 3.4577\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1541 3 540085.3766320769 3.6745\n",
      "12 yoyos for tract,wedge,wedgePop,r= 1541 3 276669.2463312176 3.5661\n",
      "13 yoyos for tract,wedge,wedgePop,r= 1541 3 412855.80569450965 3.6203\n",
      "14 yoyos for tract,wedge,wedgePop,r= 1541 3 343267.17928549286 3.5932\n",
      "15 yoyos for tract,wedge,wedgePop,r= 1541 3 379686.08886739897 3.6067\n",
      "we have 2 non-opposing shorted wedges for tract no 1542\n",
      "I am working on tract number 1560 of 1765 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1574 5.0 2 90.0 1.576 204163.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1574 6.0 2 90.0 1.5439 204163.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1574 7.0 2 90.0 1.5124 204163.2\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1575 3 365628.9719085749 1.8219\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1575 3 141627.84204922183 0.911\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1575 3 366057.1505768068 1.8288\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1575 3 238035.11295186356 1.3699\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1575 3 149913.30260531462 1.1404\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1575 3 163714.5353947081 1.2552\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1575 3 188080.491114614 1.3125\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1575 3 205931.1356443421 1.3412\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1577 1 324918.8465596076 1.8453\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1577 1 87629.29764834096 0.9226\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1577 1 326210.3466725592 1.8559\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1577 1 137874.67281755546 1.3893\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1577 1 282896.9107059968 1.6226\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1577 1 254439.83475605235 1.5059\n",
      "12 yoyos for tract,wedge,wedgePop,r= 1577 1 191128.21164473804 1.4476\n",
      "13 yoyos for tract,wedge,wedgePop,r= 1577 1 233376.33815419086 1.4768\n",
      "13 yoyos for tract,wedge,wedgePop,r= 1577 1 215499.61211282728 1.4622\n",
      "13 yoyos for tract,wedge,wedgePop,r= 1577 1 203984.68355443905 1.4549\n",
      "I am working on tract number 1580 of 1765 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1581 6.0 3 90.0 2.0284 203314.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1581 17.0 1 90.0 2.1107 203088.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1581 18.0 1 90.0 2.0759 203088.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1581 19.0 1 90.0 2.0417 203088.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1581 20.0 1 90.0 2.0081 203088.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1581 21.0 1 90.0 1.975 203088.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1581 22.0 1 90.0 1.9425 203088.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1581 23.0 1 90.0 1.9105 203088.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1581 24.0 1 90.0 1.879 203088.6\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1581 1 203088.64971889905 2.1137\n",
      "loop31.0, tr1581,wedgePops1417685.69, 212825.6, 19055.6, 927511.6, 258292.9, Overedge?1, 1, 0, 0, ,Satisfied?0000,yoyo?0001 \n",
      "   targetWP, latest drx4 are tWP,dr, 281365.0,1.2953, 281365.0,-1.0569, 281365.0,0.7805, 281365.0,-0.0008\n",
      "loop32.0, tr1581,wedgePops736664.66, 212825.6, 19055.6, 227697.5, 277086.0, Overedge?1, 1, 0, 0, ,Satisfied?0000,yoyo?0012 \n",
      "   targetWP, latest drx4 are tWP,dr, 281365.0,1.2953, 281365.0,-1.0569, 281365.0,-0.5575, 281365.0,0.01\n",
      "loop33.0, tr1581,wedgePops745264.05, 212825.6, 19055.6, 232816.4, 280566.5, Overedge?1, 1, 0, 0, ,Satisfied?0000,yoyo?0022 \n",
      "   targetWP, latest drx4 are tWP,dr, 281365.0,1.2953, 281365.0,-1.0569, 281365.0,0.0371, 281365.0,0.0018\n",
      "loop34.0, tr1581,wedgePops930078.25, 212825.6, 19055.6, 416993.9, 281203.2, Overedge?1, 1, 0, 0, ,Satisfied?0000,yoyo?0022 \n",
      "   targetWP, latest drx4 are tWP,dr, 281365.0,1.2953, 281365.0,-1.0569, 281365.0,0.2652, 281365.0,0.0003\n",
      "loop35.0, tr1581,wedgePops779405.29, 212825.6, 19055.6, 266320.9, 281203.2, Overedge?1, 1, 0, 0, ,Satisfied?0001,yoyo?0032 \n",
      "   targetWP, latest drx4 are tWP,dr, 281365.0,1.2953, 281365.0,-1.0569, 281365.0,-0.1545, 281365.0,0.0003\n",
      "loop36.0, tr1581,wedgePops790019.23, 212825.6, 19055.6, 276934.9, 281203.2, Overedge?1, 1, 0, 0, ,Satisfied?0001,yoyo?0042 \n",
      "   targetWP, latest drx4 are tWP,dr, 281365.0,1.2953, 281365.0,-1.0569, 281365.0,0.0126, 281365.0,0.0003\n",
      "loop37.0, tr1581,wedgePops793704.22, 212825.6, 19055.6, 280619.9, 281203.2, Overedge?1, 1, 0, 0, ,Satisfied?0001,yoyo?0042 \n",
      "   targetWP, latest drx4 are tWP,dr, 281365.0,1.2953, 281365.0,-1.0569, 281365.0,0.0042, 281365.0,0.0003\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1593 3.0 0 90.0 3.0337 219802.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1593 8.0 1 90.0 3.946 283223.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1593 9.0 1 90.0 3.8092 283223.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1593 10.0 1 90.0 3.6771 283223.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1593 11.0 1 90.0 3.5496 283223.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1593 12.0 1 90.0 3.4266 283223.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1593 17.0 1 90.0 4.3772 283223.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1593 18.0 1 90.0 4.2254 283223.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1593 19.0 1 90.0 4.079 283223.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1593 20.0 1 90.0 3.9375 283223.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1593 21.0 1 90.0 3.801 283223.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1593 22.0 1 90.0 3.6693 283223.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1593 23.0 1 90.0 3.5421 283223.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1593 24.0 1 90.0 3.4193 283223.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1593 29.0 1 90.0 4.3788 283223.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1593 30.0 1 90.0 4.227 283223.2\n",
      "loop31.0, tr1593,wedgePops807639.67, 127022.7, 283223.2, 126846.6, 270547.1, Overedge?1, 0, 0, 0, ,Satisfied?0011,yoyo?0511 \n",
      "   targetWP, latest drx4 are tWP,dr, 270282.8,1.6654, 270282.8,-0.1465, 127022.7,0.0043, 270282.8,-0.1033\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1593 31.0 1 90.0 4.0805 283223.2\n",
      "loop32.0, tr1593,wedgePops807639.67, 127022.7, 283223.2, 126846.6, 270547.1, Overedge?1, 0, 0, 0, ,Satisfied?0011,yoyo?0511 \n",
      "   targetWP, latest drx4 are tWP,dr, 270282.8,1.6654, 270282.8,-0.1415, 127022.7,0.0043, 270282.8,-0.1033\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1593 32.0 1 90.0 3.939 283223.2\n",
      "loop33.0, tr1593,wedgePops807639.67, 127022.7, 283223.2, 126846.6, 270547.1, Overedge?1, 0, 0, 0, ,Satisfied?0011,yoyo?0511 \n",
      "   targetWP, latest drx4 are tWP,dr, 270282.8,1.6654, 270282.8,-0.1366, 127022.7,0.0043, 270282.8,-0.1033\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1593 33.0 1 90.0 3.8025 283223.2\n",
      "loop34.0, tr1593,wedgePops807639.67, 127022.7, 283223.2, 126846.6, 270547.1, Overedge?1, 0, 0, 0, ,Satisfied?0011,yoyo?0511 \n",
      "   targetWP, latest drx4 are tWP,dr, 270282.8,1.6654, 270282.8,-0.1318, 127022.7,0.0043, 270282.8,-0.1033\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1593 34.0 1 90.0 3.6706 283223.2\n",
      "loop35.0, tr1593,wedgePops807639.67, 127022.7, 283223.2, 126846.6, 270547.1, Overedge?1, 0, 0, 0, ,Satisfied?0011,yoyo?0511 \n",
      "   targetWP, latest drx4 are tWP,dr, 270282.8,1.6654, 270282.8,-0.1273, 127022.7,0.0043, 270282.8,-0.1033\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1593 35.0 1 90.0 3.5434 283223.2\n",
      "loop36.0, tr1593,wedgePops807639.67, 127022.7, 283223.2, 126846.6, 270547.1, Overedge?1, 0, 0, 0, ,Satisfied?0011,yoyo?0511 \n",
      "   targetWP, latest drx4 are tWP,dr, 270282.8,1.6654, 270282.8,-0.1228, 127022.7,0.0043, 270282.8,-0.1033\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1593 36.0 1 90.0 3.4205 283223.2\n",
      "loop37.0, tr1593,wedgePops807005.05, 127022.7, 282588.6, 126846.6, 270547.1, Overedge?1, 0, 0, 0, ,Satisfied?0011,yoyo?0511 \n",
      "   targetWP, latest drx4 are tWP,dr, 270282.8,1.6654, 270282.8,-0.1186, 127022.7,0.0043, 270282.8,-0.1033\n",
      "loop38.0, tr1593,wedgePops590898.13, 127022.7, 66481.7, 126846.6, 270547.1, Overedge?1, 0, 0, 0, ,Satisfied?0011,yoyo?0511 \n",
      "   targetWP, latest drx4 are tWP,dr, 270282.8,1.6654, 270282.8,-1.651, 127022.7,0.0043, 270282.8,-0.1033\n",
      "loop39.0, tr1593,wedgePops647466.34, 127022.7, 123049.9, 126846.6, 270547.1, Overedge?1, 0, 0, 0, ,Satisfied?0011,yoyo?0611 \n",
      "   targetWP, latest drx4 are tWP,dr, 270282.8,1.6654, 270282.8,1.2638, 127022.7,0.0043, 270282.8,-0.1033\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 0 4.15006 122864.8251 127022.7411 1\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 1 2.91473 66481.6629 123049.8711 0\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 2 1.01002 125855.9867 126846.6232 0\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 3 4.92935 272840.5448 270547.1066 0\n"
     ]
    },
    {
     "data": {
      "image/png": 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bcLB3zPZjfPjtm/yychbHt1zH3s4h2/dvztLTNZt23mJiYBhHTsTi5myLj7cb6emaX1ZGcCs6lQa1ixDg70HzRk7S6zUfkuUHRJZF3YzA780uFHIoTOAPy3OksINpaCYxKZHdB7flyP7NmZWV4uXmJVgzpyaLpz5N5QoOTJgdxvzlEfh0cGPkwDJcuZpI7xEnadP7D9ZsvkFamtz1Kh5N2uzlY4lJifQb1Y0btyJZMXMbpd08cuxYDeo0xdGhEJt2rs3SEgb5iVKKJs850eQ5J44cj2HSnDCmzgvHwd4Knw5uOBe3Zdm6SF579wwVytoxzNeDLu1cKGAr12jif8mwTD6ltWb4B31ZuWExM75ZTLuWnXP8mAPf8eH3P/dzaN1fMrSQSWcuxDNpThirNkZhbaXo2s6FcmXsWbPpBsfPxFHKrQBD+rjzSic37O2sjY4rcogMy4hM+3Hml6zcsJgxw7/IlcIOpt6q16OucvzM0Vw5niV4qqIDEz+vwu7ldenZ0Y3l66MYNyWUip72fDDCE093Oz767i/qex3ip1lXuBMjd70KEynu+dCqDYv57ufP6O7Vh2G+b+facVs+3walFJt25v3eqnlNWXc7xr5bkX2r6/Fab3c2777FFxNCKeRozQcjPKn1dGG+mXqZ+l6H+GriJaJuJhsdWRhMhmXymcN/7qf74FbUevo5Fk5eT8ECuXtHpHe/ZqSmprBu7r5cPa6lib6bSmDwNWYtvMrtO6k0qlOElo2Lcex0LCGbb1KwgBU9vV0Z0tcdj1LSBtDcyVRI8Uhh10Jp79sYR/tChATtpriTc65nmDD7a8ZN+YjD6y9R0qV0rh/f0sQnpLFgRQTT5odzPTKZmtUcaf+iMxdDE1i+PgqtoVMbZ4b7eVC5vExBNVcy5i7+VWxcDH5vdiY5OYmgH1cYUtjBNO4OsHXPr4Yc39I42Fsz8JXS7F1Zl+8+qMTd2DTGTgrl8J8xvDmwDL27uLF2y01a9DjCgLdPcfREjNGRRS6RK/d8IC0tDf+3urJ93wbm/7SGZg0fub5bjtJa06BDZZ6uUpPAH5Y/fgORJWlpmpAtN5g0J5yTZ+NwL1kQnw6uxCemsXBlBHdi0mjWwIkAfw8a1S0is5bMxJNcucs893zgsx9Hs2X3Osa+O8nQwg6mudytm7Zn8ZogEhITsLezNzSPpbG2VnR8yQXv1s5s3XObiYFh/DDjCs7FbenTrRQKWLQ6gu6vHafOM4UJ8PegVZNiWFlJkbc0Mixj4eYunc7MhRPo3yuAvt0GGR0HMA3NJCTGs/fQdqOjWCylFC82Kc7KWTVZPqMGz1R1ZFJgGHOCr9HpZRfeGlSGqJvJ+I88ReteR1nxaxSpqXLXqyWRYRkLtnP/FnqP8OKFhi8x54flWFvnjZtcEpMSeaZVKbq2e5Wvx0wyOk6+8efpWCbNCWPtFtNsmu5erpR2K8CKX6M4ezEBT3c7hvq6093LlYIF5LovL5HZMuK+85dO08GvKaXdPFg5aweFC+WtVQUHvN2doycPczDkgoz75rLzl+KZEhTOsnVRAHRu60Llcvas23qToydNC5cN6u1Ony4lcXTIGxcE+Z3MlhGAqbWd7xudKVCgIHPGr8hzhR2gVZP2XIsI48TZP4yOku9UKufADx9XZu+quvh2L8maTTcYOzmU0iUL8sEITyqVd+DzHy9R3+sQ3/98mVvRKUZHFk9AiruFSUpOYsDbPbgWGcas75ZSpnQ5oyM91ItN2gLSwMNI7iUL8tmoChwIqUeAvwe7D0TzxYRQbG0UH4zwpH6tIvww4woNOhzisx//4npUktGRRRbIsIwF0Vrz5qcDWBIyj8lfzKVTm55GR3okL9/GAIQE7TE4iQC4G5vK3KXXmfHLVW7cSqFezcK81Kw4p87HsXrTDaytFN29XBnq6045D5nllJtyZFhGKWWnlDqglPpDKXVCKfVpxvOLlVJHMx6XlFJHnzC3yCZTgr5jScg8Rg78IM8XdoBWTdtz5MRBom5GGB1FAEUK2TDcz4PfVtfly3cqcC3S1AbwzMUE3n7Nk+5erixdG0nTLr8z7P0znDofZ3Rk8QiZGZZJAlpqrZ8FagFtlFINtdY+WutaWutawDL+v1m2MMC6rSv4atL7dHrZh5GDPjQ6Tqbcu1t1y+71BicRD7K3s8avRyn2rKzDj59UJjk5na8nh7Ln0B0C+pXBv0cpNu26RaueR/F78ySHjt01OrJ4iCwNyyilHIDdwBCt9f6M5xRwGdMPgEc2yZZhmZxx7NTvdB7QgupVarJk2ibsCprHQlFaa559yYOihZ3YtfyE0XHEv0hP1/y6/RYTA69w7FQcJV0K4OPtSkqK5pdVEUTfSaVRXVMbwGYNpA1gTsixqZBKKWvgMFAJmKy1Hv3Aa82AHzJzYCnu2e9aZDhevo2xtrZhbdAeXEq4GR0p07TWeDxnWpXyQMh53EuWNTiReBStNbv232FC4BX2Hb5LsaI29OroRoECikWrI+8vXBbgX4Y2zYvLXa/ZKMeWH9BapwG1lFJOwAqlVA2t9fGMl3sBCx8RahAwCKBsWfnHm53iE+LwH9mFmLi7rJq9w6wKO5gahgA8W70uLiVKGpxGPI5SimYNnWjW0IlDx+4yKTCMKXPDcXSwoqe3G8WcbFkaEsnAd05Tubw9Q3096NzGGVsbmZRnhCzPllFKfQzEaa2/U0rZAOFAXa112OO2lSv37JOens6g0T5s2LGGwB+W06pJO6MjZcmqDYsZ+n4furXvzY+fzJJf5c3UqfNxTJ4TxqqNN7C1UXRr70qZ0nas2hjFqXPxuJcsyNC+7vh4u0obwP8gR4ZllFIuQIrWOlopZQ9sBMZprUOUUm2AMVrrFzJzMCnu2eerie8xOeg7Pn3rewb0CjA6TpbcaxjybPV6LJrya643DBHZ71JYAlOCwlkSEklausa7tTPVKjmycectDh2Lwbm4LQNfKU3fbiUpUkjWK8yqnCruNYEgwBrT7JpgrfVnGa/NAX7TWk/LzMGkuGePxauDGPnZQPp0HcTYdyea1VVvXmgYInLO9agkpi+4yrxl14lPSOelZsWpX6swuw/eYfu+aIoUssa3eykG9CqFc/ECRsc1G7K2TD6w7/BOeg1rS8M6zZg3YTW2NrZGR8q02LgYOvV/gfDrV1gduJPK5asZHUnkkFvRKcwJvsasRdeIvptK4+eK0ryhE0dOxLJ+m2nhslc7uzG4tzvuJeU3t8eR4m7h/rpyHi+/JjgXc2F14C6KFnYyOlKmPdgwZN5Pq3mhYWujI4lcEBuXyvwVEUyfH07EjRRqVS9E25YlOH8pgRXrTQuXdW3nwlBfdyqVkzaA/0aKuwWLvnsbb/+m3Iq+SUjQbsp5VDQ6UpZ88sPbzPjlJ756dyK+3QYbHUfkssSkdJaujWRKUDih4YlUqWBP5zYuXItMJnhNJEnJ6bRrWYIAfw+eqVrI6Lh5jhR3C5WSmkLvER3Y//suFk35lYZ1mhodKUvmLZvBu2OH0b/ncD4b9YPRcYSBUlM1azbfYFJgGKcvxFOmdEF6eLkSE2dqAxgTl0aL501tABvULmp03DxDirsF0loz+qthLFgxkx8+nolPh75GR8qS+w1DGrQm8Ifl2NjITAlhuut1825TG8Df/4zBpYQtPb3d0MDClRHcvJ3Cc8+a2gC2bFzMrCYN5AQp7hZoxi8T+OSHUQz3e5sxw780Ok6W3GsYUsrVnVWzd+bJdeWFsbTW7Dt8l4mBYezcH03Rwtb07OiGo701i9dEEn49iepVHBnu547Xi85YW+fPIi/F3cJs2rUW/5FdaNPcm+njFmNlZT53+t2KvkEHv6bExN1lbdCePLuuvMg7/jgZw6TAcNZtu4m9nRU+HVxxdS7AsnVRXAhNoFwZO4b5utO1Xf5rAyjF3YKcPHeMTv2bU6FsZZbP2IqDvaPRkTItKTmJXsPacvTEQYKnbaJezYZGRxJm5OzFeCYHhbHi1yislKJzWxcqetqzdssN08JlrgV4rbc7r3Z2w8E+f9z1KsXdQkTeuI6XXxPS0lIJCdpDKVd3oyNlmrk1DBF515WriUybF87CVRGkpGrav1iCmlULsXXPbfb9blq4rH+v0vj3KIVTEcv+LEeKuwVISEyg+2utOX3+OCtmbuOZqrWNjpQlk+d8y1eT3mfkwA94a/BHRscRFiDyRjIzF14laOl1YuPSaNm4GM/XLcJvR+6yeddtHB2s8O1WioGvlMbV2TLvepXibua01gx9rzdrNi9l5rfBtGne0ehIWbJu6woGvuNDx5d6MPnLefl+hoPIXndiUpkTfI2ZC69yKzqVBrWL0KpJMY6fiWPNZtPCZT7ebgzp405Zd/PoaZBZUtzN3Hc/f8r4GV/yfsBXDPUdZXScLLnXMKRa5WdYMm0T9nbSY1PkjITENH5ZGcHUeeFci0imxlOOeLVyJjQsgWXrokhL13R8yYXhfh48VdEy7nqV4m7Glq//hYAP/fDp4Mv3H003q6veBxuGhMzZjauzrM0ucl5ySjrL10UxOSiMi5cTqVDWjq7tXLlxK4VFqyNISEynTfPiDPfzoHaNwkbH/U+kuJupg3/so8drranzTAMWTl5PAVvzGTeMT4ijy8CWXLx8jpWztlO9ck2jI4l8Ji1Ns27bTSYFhnH8TByl3ArQ09uNxMR0Fq6KIPpuKk3qF2W4nwdNnitqVhdO90hxN0NXrl6ivW9jCjsWYc2c3RR3KmF0pEwz94YhwrJordm+L5qJgWHsP3KX4k42vNLJDSsrxaJVEUTeTKH204UI8PegdTPzagOYY232RM6Iib2L7xudSE1NIejHFWZV2AHGTfmQ9dtW8cnI76SwC8MppWjxfDFaPF+MA0dNd71OmhNOIUdrenq74lTEhuCQSPqNOs1TFRwY5udOx5dcsLExnyKfFXLlbpDU1FT8R3Zhx/5NLJi4lqb1WxodKUsWr5nLyE8H0LvLQL4eM8ksf9UVlu/4mVgmzQkjZLNpDflu7V3wKGXHyl+jOH0hnrLuBRnSx50eHdywK5h373qVYRkz8tF3I5m1aBLj3ptC7y4DjI6TJb/9voueQ9vQoE5T5k9YY1YNQ0T+dCE0galzw1i6NgqtoePLzjxVwYH1229y5HgsriVsGfRqafp0LUkhx7w3oJFTbfbsgJ1AQUzDOEu11h9nvBYADAdSgbVa63cetS8p7iZzlkzj/XEjGPjK63wy8luj42TJvYYhJZycWR24C6cixYyOJESmXY1I4uf54SxYEUFikmk2TZ0ahdm5P5pdB+5QtLA1/j6l6N+zNMWd8s5FS04VdwU4aq1jlVK2wG7gdcAeeB9or7VOUkq5aq0jH7UvKe6w47dN9Hndm+aNXibw+2VYW5vP2hj3GobcjL5ByJzdlC9TyehIQjyRW9EpzFp0lcDF17gTk0azBk40a1CUQ8di+HX7LeztrOjdpSSDe5emlKvxbQBzfFhGKeWAqbgPAd4CpmutN2d2+/xe3M9ePIm3fzM8SpVl5awdFHI0n7m3DzYMWTh5PY3qNjM6khD/WUxsKvOWXWf6L1eJuplCnWcK0+aF4py+EM+qjaaFy7p7uTKkrzsVyhp3Y16OFXellDVwGKgETNZaj1ZKHQVWAW2ARGCU1vrgQ7YdBAwCKFu2bN3Q0NCs5LMYN29H4eXXhITEeELm7MajlKfRkTLtbw1DPpqBj7ev0ZGEyFYJiWkEh0QydW44V64mUbWiAx1fdib8ehJLQiJJSdV4vejMcH8Pnq6S+yu05saVuxOwAggAFgFbMQ3RPAcsBiroR+wwv165JyUn0XNoG/44eYil07dQp0Z9oyNlyb2GIcN8R/FewFdGxxEix6SkprN64w0mzQnj7MUEPN3t6O7lQvTdVBatjiQ2Lo0XmxQjwM+D52rlXvOZXJkto5T6GIgDWgFfa623Zzx/AWiotY76t23zY3HXWvPGJ/1ZunY+U76aT8eXehgdKUvMuWGIEE8qPV2zcectJs4O4+jJWNycbfHxdiMtTbNwVQS3olNpWKcIAf4evNDQKcenAufUB6ouQIrWOlopZQ9sBMYBHkBprfVHSqkqwBagrFy5/92E2V8zbspHjBr8EW8O/MDoOFlizg1DhMgOWmt2H7zDxMAw9hy8g1MRG3p1dMPOzopFqyPuL1wW4O9B2xYlcqwNYE4V95pAEGANWAHBWuvPlFIFgNlALSAZ05j71kftK78V95DNyxj8bi86t+nJxM+DzOpGH3NuGCJETvj9eAyTAsPYsOMWDvZW9PR2o0QxW5aui+Svy4lU9LRnmK87ndu6UMA2e3/DlZuY8pCjJw7RddCLPP3UswRP3YhdQfNZX9rcG4YIkZNOn49jclA4qzZGYW2l6NrOBU8Pe9ZsusGJs3GUdivAkD7u9Orkhr1d9kx1luKeR4Rfv4KXb2MKFrQjZM5unIu7Gh0p0+41DFm9aQkzvw2mbYtORkcSIk8KDUtk6rxwFq+OIDVN06GVMzWecmTz7tvsP3KXEsVsGdCrFL7dS1G08H+761WKex4QFx9L5wEtCA2/yKpZO6haqYbRkbLk+58/44cZX/De8C8Z5ve20XGEyPMibiQzY8FV5i67Rlx8Oq2bFqNB7SLsPXSXrXtvU9jRGr8epRg1uOwTL1Imxd1gaWlpDHi7O5t3ryNo/EpaNm5jdKQsWfHrQoZ/4EuPDn354aMZZvUZgRBGu30nxdQGcNE1ou+k/u01G2vF5kW1qFz+yTpDSXE32Bc/vcvUeT/w+ajx9Os5zOg4WXLo2G/0eK01tWvUN7uGIULkJXHxabz06lEuXUnki7crUKWiPdUqOf6ntWpkPXcDLVwZyNR5P+Db/TX8fYYaHSdLrly9RP9R3Sjl6sGMbxZLYRfiP3B0sGbPirpGx5Dinh32HtrBu2OH8ULD1nz21g9mNZwRE3sXvzc7k5ycRND0zRR3cjY6khAiG0hx/48uXj7HwHd6UL5sJaaOXYCNjfmc0tTUVIa+15tzl04zf0IIlcpVNTqSECKbmE8lyoNu37mF7xudUMqKoPErKVrYyehIWfLZT6PZuvdXxr47iWYNXjQ6jhAiG0lxf0IpqSkMGt2TsGuhLJ66AU+PCkZHypKgpT8za+FEBvQaQd9ug4yOI4TIZlLcn4DWmve+DmDvoe38+Mks6tdqbHSkLNn522Y+/PYNXmzSjo/eGGd0HCFEDpAl/p7Azwt+5JeVswnwH013rz5Gx8mSc3+dYvC7vahSvhpTvpxnVp2ghBCZJ8U9izbuWMMXP71Lu5adeWfIp0bHyZKbt6Po+0YnCha0Y874FWbVCUoIkTUyLJMFx88cZdgHfXmmam0mfBZoVmubJyUnMeDtHkREXWXp9C1m1QlKCJF1UtwzKeLGNfxGdqFokWIE/rAce7snu43YCFpr3vlyCAeO7mHKV/PNrhOUECLrpLhnQkJiPP1GduXO3dusmLmNki6ljY6UJZPmfMPStfMZNfgjs+sEJYR4MlLcHyM9PZ3XP+7HH6cOM+vbJdR4qpbRkbJk7ZblfD35Qzq97MMbA943Oo4QIpc8dtBYKWWnlDqglPpDKXVCKfVpxvOfKKXClVJHMx7tcj5u7vvu509Zu2U5H4wYy8vNvY2OkyV/nDzMiI/8qVuzId/LKo9C5CuZuXJPAlpqrWOVUrbAbqXU+ozXxmutv8u5eMZatm4BP80aS6+O/gzu/abRcbLkakQY/iO74FzclVnfLjGrTlBCiP/uscU9o+F1bMZfbTMeubdOsEEOHt3LqM8H06juC3z17kSzuuqNi4/F783OxCXEsmrSDlxKuBkdSQiRyzI1l08pZa2UOgpEApu01vszXhqulDqmlJqtlCqWUyFzW2jYRfqN6oZ7qbJmtwRueno6AR/6cur8n0z5cr7ZdYISQmSPTBV3rXWa1roW4AHUV0rVAKYCFYFawDXg+4dtq5QapJQ6pJQ6FBUVlS2hc9Ld2Dv4jexMenoaQeNXUqxocaMjZcnYSe+zYccaPhn5HS82aWt0HCGEQbJ0F47WOhrYDrTRWkdkFP10YAbw0MnTWuvpWut6Wut6Li4u/zVvjkpNTWXImFe5GHqOn8ctoqJnFaMjZcmiVXOYMvd7+nYbTD8f8+oEJYTIXpmZLeOilHLK+LM90Ao4rZQq9cDbOgPHcyRhLvrkh1Fs37eRsWMm0eS5FkbHyZK9h3Yw+quhNGvQis9GmVfDECFE9svMbJlSQJBSyhrTD4NgrXWIUmqeUqoWpg9XLwGDcyxlLghcPIXA4CkM7v0mr3TqZ3ScLLl4+RwDR/tQrkxFpn39C7Y2T96rUQhhGTIzW+YYUPshz5vXcoiPsG3vBj76fiStm7bn/YCvjI6TJdF3b5sahqAI+tH8GoZYkvR0TeWm31K3pjvBU181Oo7I5/L9Haqnzx/ntTGvULVSDSab2RK49xqGXLl6icVTN1DOo6LRkfK1Om0nkJiUSnx8itFRhMjfxf3GrUj8RnbBwc6ROT+swNGhkNGRMk1rzfvjXmfPwW2M/2QmDWo3MTpSvjby0xCibsYBsHJWX4PTCJGPi3tiUiL9R3Un6uZ1lv68BfeSZYyOlCXTF/zEghUzGe7/Dj28pJgYadGqP1i85hgAf25+Axsb81kKWliufFnctda8/cVgDh3bx9SxC6hd4zmjI2XJxp0hfP7TaNq17MzoIZ8ZHSdf++PkNd76fC0A6+f5U9zJfJaCFpYtX15i/DRrLMvXL+Tt1z7Bu3V3o+NkyYmzfzDs/T5m2TDE0ty8HUe7voEA/PBRe2pWK/WYLYTIPfmuMqzetIRvp31Cl7av8Hr/MUbHyZKIG9fwe7MLRQo7mV3DEEuTmppOzdY/AdDT+1l8vJ81OJEQf5evhmWOHD/Im5/057lnn+e7D382qxt9EhIT6DeyK7fv3GSlGTYMsTSeDb8GwM25EN9/1N7gNEL8r3xT3MOvX8b/rS64Opdi1ndLKFigoNGRMi09PZ03PjE1DJn5bTA1qv7PbQciF/kM/eX+nw+tCzAwiRD/Ll8U99i4GHzf7ExiYgKLp/xKiWJ5e42bf/p++meEbF7GByPG0qZ5R6Pj5GsTA/ey+8AlAM7uHIWVlfn89ifyF4sv7mlpaQz/oC9nLpxg3k+reari00ZHypLl63/hx5lf0dPbj9f6jDQ6Tr6287e/+HrydgB2r3gNRwfzWQpa5D8WX9y/nPgem3at5ct3fqJ5o5eMjpMlB4/u5a3PBtGoTjPGjplkVp8RWJorV6PpNXwhAEE/9qB8GfNaClrkPxY9W2bBiln8PH88/j2G4tdjiNFxsuRy+F/0f7s7pUuWZbqZNQyxNAmJKTT0ngLAmwOb0KpJJYMTCfF4Flvcdx3YyntfB9C80Ut8MtK82rzejb2D75udSE1NIWj8Coo7lTA6Ur6ltaZSk28BqPOMO6MGNzM4kRCZY5HDMucvnWHw6J5U8KzM1LELsLExny8zNTWVoe/15mLoOeZPDKFSuaeydf8nz12kgK0tlcqZ13ILRmna9ef7f149W5Z5EObDfKpeJt2Kvonvm52xtrYhaPxKihQqanSkLPl0/Nts27uBb96fStP6LZ94PwmJiZz76zJnLoRy5sIlzlwM5ezFUMKuRQDw9mu++PXwxqlI4eyKbnE+/HYjf12+BcBf+0bLZx7CrFhUcU9OSWbQOz5cvX6Z4KkbKete3uhIWTIneCqzF09m0Ktv8Grn/pnaJiExiQuXrnDm4iXOXgzlzAVTEb989TpaawAK2NpS0dODus9Uo3L5Muzaf4RvpwUxdd4SfLt1YOArXXApYTH9zbPFqg0nmL34EABHfh1BAVvzWQpaCLCg4q61ZszY4ez7fScTPgvkuVrPGx0pS7bv28hH34+kVdN2fDBi7P+8npSczIXQMM5eCP3/Qn4xlNCwa6SnpwNgY21NBU8PnqlWme5eralSwZOnKnpSzsMdG5u/F6fjZy4wec4ips5bwqxFK/DxfpkhfbpTpnTJXPl687JT5yMZ+v4qwLR8r6uz+SwFLcQ96t7V3b++QSk7YCdQENMPg6Va648feH0U8C3gorW+8ah91atXTx86dOg/h36YqXO/54sJY3i9/xjeGfJpjhwjp5y9eBJv/2Z4lPYkeOomom7evT+Mcub8Jc5cvMSlsKukpZmKuLW1FeXLuGcU73Km/1bwpHxZdwrYZq3F3sXL4UydG8ySkE2k63Q6vdyS4X4+VKngmRNfap4XfTeBp1uOB+DL0S/j172uwYmEAKXUYa11vSxtk4nirgBHrXWsUsoW2A28rrX+TSlVBpgJVAXqGlXcf92+igFv96D9i12Y+tUCs1gpMTExid0H97Ln0CEWr15CYpIVpUtW5Up4BKlpaQBYWVnh6VGKpyp4ZhTwclSp6ElFTw8KFsjeqZFXI6KYvmAZ85evJSExibYtGjPcrye1ns7eD3TzsrS0dMo2MK0Z06F1NaaN7WxwIiFMcqS4/+MADpiK+xCt9X6l1FLgc2AVUM+I4n789BE6DWjBUxWqs3T65jy3UmJycjJ7D+9nz8GDHDt1mr+uXOPGrTiSkq34/5mompIuJXimahWeqphRyCuWp6KnB/Z2ubsGzq3oO8xatJLAxau4ExNL0/q1CejXi+frPmvxHyiWa/g1KanpONjbcm7X20bHEeK+HCvuSilr4DBQCZistR6tlPIGXtRav66UusS/FHel1CBgEEDZsmXrhoaGZiXfI12Pukp738ZYKStCgnbj5mzcetppaWns+/0gew7s549Tp7l4OZyom7EkJj1YxMHaOhWnIgUoW9qFapUr0rBOHVo835TiTnnrA82Y2DjmL1/LzwuWEXXzNrVrVGWEfy9aNW1gFr8ZZdWAt5eyfttZAK4cGCNrxog8JTeu3J2AFcDrwAzgJa31nUcV9wdl55V7QmI8XQa25PylM6yctZ2nq+TOetrp6emEX7/MmYsnOXvhJKcvnODsxZOcOhdDaprH/fdZW6VQtEgBPEq5UL1yBZ6rVYsWzzfFzdm8Fi1LTEpm8ZoNTJ0bzJWrEVStWI5hfj54t27+Px/SmqtZiw7y0XebADi1fSRFCtkZnEiIv8vx4p5xkI+BdCAAiM942gO4CtTXWl//t22zq7inp6cz+N1erN+2ktnfL+OlZl7/eZ//pLXmakQYZzKK971ifvavU8QnxN1/X0mX0lSpUB0354r8eSaG0+ev4mBvRz+fjgzoZTlTDFNT01i1cRuT5yzmzMVQPN1LMaRvd7p7vYRdQfNdGmH/kct0GTgfgG3BA6lSwbx++Ir8Iac+UHUBUrTW0Uope2AjME5rHfLAey6Ri1fuX0/+kImB4/jojW8Y3PuN/7QvrTXXo66aCviFk/cL+bmLp4iJu3v/fS4l3KhSoTpVKz5NlQrVeapCdSpXqIZTkb8X73tTDNds3knBArb09G7DkL7d8Sjl9p9y5hXp6els2vkbEwMXcuTEGVxLFGdw76707tKeQo556/OOx7kWGUO9dhMB+Pnrzni1qmZwIiEeLqeKe00gCLDGNHgcrLX+7B/vuUQuFfclIfN445P+vNq5P+Pem5LpD/m01kTdjDBdgT9QyM9ePMmdmOj77ytRzOV+8a5SMeO/FapneX2XC6FhTJ0bzNK1m9Fa07ltS4b5+lC5fNks7Sev0lqz++BRJs1ZxO4DR3AqUhh/n4708+lEcaciRsd7rKTkVCo8/w0Ag16tz8dvtjI4kRD/LleGZf6L/1rc9x/Zjc+Ql6lfuwkLJoZga/PwOd03b0f97Sr8zIUTnLl4kug7t+6/x6lo8fuF+8FC7lzc9YnzPczViCh+nr+UBSvWkZiUTNvmjRnu35Nnq1fJ1uMY6ffjp5gUuIgNO/bhYG9H7y7tGfRqV0q5Ohsd7aG01ng8Z7pR7KkKzmwNHmRwIiEezaKL+6WwC3j5NqFY0eKsmbMbpyLFuH3nFmcvZnyoeeHk/avym7ej7m9XpFBRUwGvaCrk94ZWXEq45erUvn9OMWzWoA4B/r1oVLemxUwxPH3+LyYHBbNq4zasrazp7tWKIX17UL6Mu9HR/qZN79n8edr00VDYwTEWc/6F5bLY4n4nJhpv/6acv3SGlo3bkpySxNkLJ4m8+f+f3RZyLEzl8tV4qkJ1nqr49P1iXtKldJ76xxsTG8e8ZWuZ/otpimGdZ6oR4N+T1k0b5qmc/8Xl8GtMnbeExas3kJKaRodWzRju35PqlSsYHY2xk7Yxac4+AC7seQe7ghazAoewYBZb3H+aNZZvpppWPLC3c6BKhWqmAn5vWKVidUq7lTGr4piQmERwyMb7UwyrVSrPMD8fOrR6wWKmGEbcuMmMBcuZuyyEuPgEWjVtwHC/njz3rDGtDjdsP0u/UUsBOBAyHPeSef+zASHAgov7reib/HHyEBU9q+BRytOibqJJSU1l9cbtTJqzmLMZUwyH+vagu1frbF9iwCjRd2MIDF7FrIUruX3nLo3q1CSgX0+aNaibaz+Qt+29QO8RiwEInvYKjeuVy5XjCpEdLLa45wfp6els3LmPibMXcfTkGdycizOodzf6dGmPo4O90fGyRXxCAgtWrGfa/KVcj7xBzWqVGe7Xk7YtGuf4D2z3el8B8OHrLXmtT8McPZYQ2U2KuwW4N8VwYuBC9hw8ilPRwvTr0RF/M5limBlJycksW7eFyUGLuXTlKpXKlWGorw9d2rbENoe6Zt2NTWTbngt0fNmYISEh/gsp7hbmn1MM+3T1YtCrXSnpYhk9VdPS0gjZsouJgYs4de4i7iVdGdKnOz07tsn1BdOEyMukuFuo/51i2Jqhvj0o51Ha6GjZQmvN1j0HmTD7Fw4dO4lzcScG9OqMb3dvihRyNDqeEIaT4m7hQsNMUwyD15imGHq3foHh/j2pVsm82gn+G601+4/8ycTARWzfd4jCjg749vBmYK/OOBe3jDV6hHgSUtzziX9OMWzdtCEB/XpR9xnLWRvlz9PnmBi4iHVbd1OwgC2vdGrLa326414ye+8gFsIcSHHPZ27fucuc4NXMXLSC6DsxNKr7LCP8e9K0QR2zmvP/KOcvXWZK0BKWrdsMQJd2LzLMtweVylnGGj1CZIYU93wqLj6BBSvW8fOCZfenGAb496JN8+ct5p6A8OuRTJu3hF9W/kpScjJtWzQmwL8nNatZzho9QvwbKe753P0phnMWcynsKpXLl2WYrw+d2rTIsSmGue3GrdvMXLiSOcGriImLp3mjegT496RB7Wcs5rcVIf5JirsATI011m7ZycQ5izl17iIepdwY0qc7Pt4vW8wUw7uxccxduobpC5Zx8/Yd6tWsTkC/XrzYuL4UeWFxpLiLv9Fas2XPASbOXnh/iuHAV7rQt1sHi5limJCYyKJVG5g6bwnh1yOpVrkCAX4+eLVqhrW1ZazRI4QUd/FQ/5xiWKSQI349vBnQqzMlijkZHS9bpKSmsuLXrUyes5jzl65Qrkxphvn60LXdixazRo/Iv3KqE5MdsBMoCNgAS7XWHyulPgc6YuqnGgn4aa2vPmpfUtyNd+zUWSbNWZwxxbAAr3Zuy+De3SxmimF6ejq/bt/LxMCFHDt1jpIuJRjcuxuvdm5nMWv0iPwnp4q7Ahy11rFKKVtgN/A6cFJrfTfjPSOA6lrr1x61Lynuecf5S5eZHBTM8nVbAOjarhVDfXtQqVwZg5NlD601O/cfZmLgYvYd/gOnooUZ0LMzfj28KVbUMtboEflHjg/LKKUcMBX3IVrr/Q88PwYoq7Ue8qjtpbjnPf8/xXA9SckptH+xKQF+PalRtZLR0bLNwT9OMGnOIjbv2o+jgz19u3ox8NUuuDlbxho9wvLlWHFXSlkDh4FKwGSt9eiM578E+gJ3gBZa66h/34sU97zsn1MMWzxfjwD/XjSo/YzR0bLNyXMXmTxnMas37cDWxpoeHV5mRL9elHZzMTqaEI+UG1fuTsAKIEBrffyB58cAdlrrjx+yzSBgEEDZsmXrhoaGZiWfyGX/nGL43LNPE+Dfi5aNn7OYKYZ/XQln6txgloRspkbVSqwJ/MnoSEI8Uq7MllFKfQzEaa2/e+A5T2Ct1rrGo7aVK3fz8c8phtWrVGC4X0+8XmxqMVMMJ8xeyLgpgRxev9BillEWlulJivtj701XSrlkXLGjlLIHWgGnlVKVH3ibN3A6KwcWeZu9nR3+Ph3Zs3IO4z8ZRXJyCkPf+4oXug9g4cr1JKekGB3xP2vdzNSRacvu/Y95pxDmJzMLj5QCtimljgEHgU1a6xDga6XU8YznX8I0g0ZYGFsbG3p4vcS24BnM+OYjCjs6MOqL8TTq6MuMX5YTn5BgdMQnVrViOdxLurJ5lxR3YXnkJiaRJfenGM5exL7fj1GsaBH69+qEf4+OOBUpbHS8LHt/3CQWrd7A8S1LLWZpBmF5cmRYRogHKaV4oWE9lk7/jpWzxlO3ZjW+mzaX+l69+XLCTCJv3DI6Ypa0atqAxKQk9h76w+goQmQrKe7iiT337NMEjf+cTQun0bppQ6bNX0pD7z6M+XoCl8OvGR0vUxrVfRYHezs27frN6ChCZCsp7uI/q165ApO/HMPOZbPo1r4Vi1ZtoEkXfwI+HMeZC5eMjvdIdgUL8ELDumze/Ru5OUQpRE6T4i6yTfky7nzz/pvsXRVE/56dWb9tNy19BtF/1CccOZ53J1O1atKAaxE3OHH2otFRhMg2UtxFtivl6szHbw7mQMgCRg7szW+//4mX3wh8ho5m98Ejee4K+cUmDQDYtGufwUmEyD5S3EWOKe5UhLcG92X/mnl8+PpAzl0MxWfIaDr4jWDD9r2kp6cbHREAlxLFqP30U2yRKZHCgkhxFzmukKMDr/Xpzt5Vc/l6zAhuRt+h36hPaNVzMMvXbyE1Nc3oiLRq2pAjJ86Y3WwfIf6NFHeRa+wKFqBPVy92LQtk0hfvopQi4MNxNO3qz9ylISQmJRuWrVVT092qW/ccMCyDENlJirvIdTY21nRu05JNC6cR+P2nlCjmxJivJ9DIuy9T5wYTGxef65merlKB0m4uMiVSWAwp7sIwVlZWvPRCI9YE/kTwtG94qqInX0yYSX2v3nw7LYhb0XdyLYtSilZNG7Bz/++G/gYhRHaR4i4Mp5Sicb1aLJoyjrVBE3m+3rP8OHMB9b1688kP07gWeSNXcrRq0pD4hET2HZa7VYX5k+Iu8pRaTz/FzG8/ZlvwDNq/2JTZi1fSyLsvb38xnouXw3P02I2fq4W9XUEZmhEWQYq7yJOqVPDkp0/fYc+KObzSuS3L1m3mhW79GTLmS06cvZAjx7QrWICm9euwedf+PDcXX4iskuIu8rQypUvy1egAfls9jyF9urN170FeemUIfd/4kINHT2T78Vo3a0j49UhOnf8r2/ctRG6S4i7Mgqtzcd4L6M+BkPm8M8SPI8dP02nAm3Qd9Bbb9x3KtivtF5vUB5A13oXZk+IuzErRwoV4vf8r7F8zl0/fGkJo+DVeDXiPtn2GEbJ5J2lp/+2GKDfnEjxbvYqMuwuzJ8VdmCUHe3sG9OrM3pVBfP/hSGLjExj87he06DGQxas3/Kc2gK2bNuTI8dPcuHU7GxMLkbsy00PVTil1QCn1h1LqhFLq04znv1VKnVZKHVNKrbjXZ1WI3FTA1paeHduwY8lMpo59H7uCBRn52fc07uTH7EUrSUhMzPI+WzVtgNaaLXsO5kBiIXJHZq7ck4CWWutngVpAG6VUQ2ATUENrXRM4C4zJsZRCPIa1tTXerV9gw4IpzPvpC8qUduPD76bQoEMfJsxeyJ2Y2Ezvq8ZTlSjpUoLNO2VoRpivxxZ3bXLvX4ZtxkNrrTdqrVMznv8N8MihjEJkmlKKlo3rs3zGDyyf8T3PVqvCuCmBNPDqzdhJs4i6+fihFqUULzZpwI79h0lKlrtVhXnK1Ji7UspaKXUUiAQ2aa3/OZWgH7D+X7YdpJQ6pJQ6FBUV9Z/CCpEVDWo/w7wJX7Jh/hSaP/8ck4OCaejdhw++mUzYtYhHbtu6WUPi4hP47fdjuZRWiOyVqeKutU7TWtfCdHVeXylV495rSqn3gVRgwb9sO11rXU9rXc/FxSUbIguRNTWqVmLa2PfZsXQWndu0ZP7ytTTu5Mcbn3zLub8uP3SbJs/Vxq5gQZkSKcxWlmbLaK2jge1AGwCllC/gBbyq5ZY+kcdV9PTguw9HsmflHHx7eLNm005a9BjIwHc+49ips397r71dQZrUr8WmXdJbVZinzMyWcbk3E0YpZQ+0Ak4rpdoAowFvrXXur9EqxBNyL+nKZ28N4UDIPEb068XuA0do22c4rwwfw77Dx+4X89ZNG3LlakSeb/ItxMNk5sq9FLBNKXUMOIhpzD0EmAQUBjYppY4qpablYE4hsl2JYk68M8SPAyHzeW94f06eu0i3waPo2P9NNu36jZaNM+5W3S1DM8L8qNz8lbNevXr60KFDuXY8IbIiITGJxWs2MHXuEsKuRVCtUnmuXIugasVyrJr9o9HxRD6mlDqsta6XlW3kDlUhMtjbFcSvuze7VwTy06fvkJqWRmxcPIf/PJWlefJC5AU2RgcQIq+xtbGhW/tWdGnbko0797H38DHsChYwOpYQWSLFXYh/YWVlRZvmjWnTvLHRUYTIMhmWEUIICyTFXQghLJAUdyGEsEBS3IUQwgJJcRdCCAskxV0IISyQFHchhLBAUtyFEMIC5eraMkqpKCA01w6Y+5yBG0aHyEPkfPydnI+/k/Pxd486H55a6yw1xMjV4m7plFKHsrq4jyWT8/F3cj7+Ts7H32X3+ZBhGSGEsEBS3IUQwgJJcc9e040OkMfI+fg7OR9/J+fj77L1fMiYuxBCWCC5chdCCAskxV0IISyQFPcnoJTqrpQ6oZRKV0rVe+D5EkqpbUqpWKXUpH9sU0ApNV0pdVYpdVop1TX3k+eMJzkfD7xntVLqeO6lzXlZPR9KKQel1NqM74sTSqmvjUmeM57w30tdpdSfSqnzSqkJSimV+8lzxr+dj4zXxmR8zWeUUi8/8HyvjPNxTCn1q1LK+XHHkeL+ZI4DXYCd/3g+EfgQGPWQbd4HIrXWVYDqwI4cTZi7nuR8oJTqAlhic9InOR/faa2rArWBxkqptjkbMVc9yfmYCgwCKmc82uRkwFz20POhlKoO9ASexvT1TlFKWSulbICfgBZa65rAMWD44w4ixf0JaK1Paa3PPOT5OK31bkzftP/UDxib8b50rbXF3Jn3JOdDKVUIGAl8kQsRc1VWz4fWOl5rvS3jz8nA74BHroTNBVk9H0qpUkARrfU+bZrxMRfolCthc8G/nQ+gI7BIa52ktf4LOA/UB1TGwzHjN5giwNXHHUeKey5QSjll/PFzpdTvSqklSik3IzPlAZ8D3wPxRgfJSzK+VzoAWwyOYiR3IOyBv4dlPGfp3IErD/w9DHDXWqcAQ4A/MRX16sCsx+1Mivu/UEptVkodf8ij4xPszgbTldgerXUdYB/wXbYGzmHZeT6UUrWASlrrFdmfNHdk8/fHvX3aAAuBCVrri9mXNudl8/l42Pi6Wc3ZfsLz8dCvWylli6m41wZKYxqWGfO4DDZPlDwf0Fq3ysbd3cR0hXqvmC0B+mfj/nNcNp+PRkBdpdQlTN+Drkqp7Vrr5tl4jByVzefjnunAOa31jzmw7xyVzecjjL8PS3mQiWGIvOQJz0cYUOaBv9/7umtl7PMCgFIqGHj3cTuTK/dckDFuuAZonvHUi8BJwwIZTGs9VWtdWmtdDmgCnDWnwp4TlFJfAEWBNwyOYjit9TUgRinVMGOMuS+wyuBYuWE10FMpVVApVR7TB8kHgHCgulLq3qqQrYFTj92b1loeWXwAnTH9lE0CIoAND7x2CbiFaRZIGFA943lPTJ+OH8M0nlrW6K/DyPPxwOvlgONGfw1Gng9MV2g64x/s0YzHAKO/DiO/P4B6mGaVXAAmkXE3vSU8HnM+3s/4ms8AbR94/rWM749jmC4USzzuOLL8gBBCWCAZlhFCCAskxV0IISyQFHchhLBAUtyFEMICSXEXQggLJMVdCCEskBR3IYSwQP8HOBcnP02TPjoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loop40.0, tr1593,wedgePops807639.67, 127022.7, 283223.2, 126846.6, 270547.1, Overedge?1, 0, 0, 0, ,Satisfied?0011,yoyo?0611 \n",
      "   targetWP, latest drx4 are tWP,dr, 270282.8,1.6654, 270282.8,1.5475, 127022.7,0.0043, 270282.8,-0.1033\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 0 4.15006 122864.8251 127022.7411 1\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 1 4.4622 123049.8711 283223.1991 0\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 2 1.01002 125855.9867 126846.6232 0\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 3 4.92935 272840.5448 270547.1066 0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I looped 40 times on tract 1593, giving up w pop 807639.6698904625\n",
      "I am working on tract number 1600 of 1765 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1604\n",
      "I am working on tract number 1620 of 1765 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1621 4.0 2 90.0 4.5739 203363.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1621 5.0 2 90.0 4.494 203363.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1621 6.0 2 90.0 4.4154 203363.5\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1623 3 2984346.3233903605 3.0291\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1623 3 1737565.102567761 1.5146\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1623 3 8598.246835052734 0.7573\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1623 3 1026479.2631718282 1.3585\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1623 3 68814.43093704514 1.0579\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1623 3 202431.71150857117 1.2082\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1623 3 459868.42336527316 1.2833\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1623 3 315559.77213149704 1.2458\n",
      "12 yoyos for tract,wedge,wedgePop,r= 1623 3 250832.29663149122 1.227\n",
      "12 yoyos for tract,wedge,wedgePop,r= 1623 3 279503.1197557646 1.2364\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1627 3 255911.7826439289 1.6127\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1627 3 1291627.2187122218 4.1843\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1627 3 1242568.1805075414 2.8985\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1627 3 1106085.2831923252 2.2556\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1627 3 981700.7496115474 1.9342\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1627 3 612213.0005741869 1.7734\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1627 3 370695.4353614835 1.6931\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1627 3 307655.76019913104 1.6529\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1627 3 280302.6092181745 1.6328\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1634 3 32452.801766201155 1.4163\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1634 3 1294210.179634229 5.2025\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1634 3 1196095.5982799311 3.3094\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1634 3 708243.1139036069 2.3628\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1634 3 72019.05214137991 1.8895\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1634 3 201608.1663309026 2.1262\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1634 3 359673.7932625904 2.2445\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1634 3 516142.9744813591 2.3037\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1634 3 426588.9071049909 2.2741\n",
      "12 yoyos for tract,wedge,wedgePop,r= 1634 3 469609.043454868 2.2889\n",
      "12 yoyos for tract,wedge,wedgePop,r= 1634 3 447803.8948687445 2.2815\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1636 2 6109953.535335428 4.1253\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1636 2 1057965.1312294248 2.0627\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1636 2 8850.424785455223 1.0313\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1636 2 920660.1134489666 1.7175\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1636 2 147168.36955834902 1.3744\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1636 2 477447.9570259413 1.5459\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1636 2 302851.2070120567 1.4602\n",
      "I am working on tract number 1640 of 1765 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1651\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1659 3 1510598.1393090356 5.4865\n",
      "I am working on tract number 1660 of 1765 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1661 9.0 3 53.7 4.8718 1206309.6\n",
      "we have 2 non-opposing shorted wedges for tract no 1663\n",
      "I am working on tract number 1680 of 1765 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1692 1 490360.2069822098 1.5537\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1692 1 1198688.2537219971 4.6189\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1692 1 1173034.9421072518 3.0863\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1692 1 1077194.4138771738 2.32\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1692 1 1053522.9356709516 1.9368\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1692 1 974111.3538811973 1.7453\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1692 1 783877.3589311276 1.6495\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1692 1 644239.9161919712 1.6016\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1692 1 568949.3540081269 1.5776\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1692 1 530017.3416851487 1.5657\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1692 1 510692.1719602362 1.5597\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1692 1 520621.51571647736 1.5627\n",
      "I am working on tract number 1700 of 1765 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1703 1 674075.4220797827 4.8494\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1703 1 275608.12146206846 2.4247\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1703 1 707341.5079196035 5.0489\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1703 1 389600.99139317754 3.7368\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1703 1 522440.8459886427 4.3929\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1703 1 564469.7286482353 4.7209\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1703 1 699310.8930243759 4.8849\n",
      "12 yoyos for tract,wedge,wedgePop,r= 1703 1 610132.081624341 4.8029\n",
      "13 yoyos for tract,wedge,wedgePop,r= 1703 1 667298.4141085962 4.8439\n",
      "13 yoyos for tract,wedge,wedgePop,r= 1703 1 644526.8504856406 4.8234\n",
      "we have 2 non-opposing shorted wedges for tract no 1704\n",
      "we have 2 non-opposing shorted wedges for tract no 1707\n",
      "we have 2 non-opposing shorted wedges for tract no 1709\n",
      "we have 2 non-opposing shorted wedges for tract no 1711\n",
      "I am working on tract number 1720 of 1765 tracts\n",
      "I am working on tract number 1740 of 1765 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1748 1 2899962.035164226 4.3383\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1748 1 89813.62394796545 2.1691\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1748 1 3466776.4539205246 4.5584\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1748 1 2420944.50078483 3.3637\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1748 1 1906287.0032498185 2.7664\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1748 1 608822.6498875809 2.4678\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1748 1 1417245.163177811 2.6171\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1748 1 930357.0820277082 2.5425\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1748 1 734584.3805715094 2.5051\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1748 1 666788.3258535982 2.4865\n",
      "12 yoyos for tract,wedge,wedgePop,r= 1748 1 637719.5118662354 2.4771\n",
      "13 yoyos for tract,wedge,wedgePop,r= 1748 1 652061.1939299923 2.4818\n",
      "we have 2 non-opposing shorted wedges for tract no 1751\n",
      "I am working on tract number 1760 of 1765 tracts\n",
      "Sum of weights over all precinct home districts should have been 1.000, but was  1.0001088050758582\n",
      "Average and max number of wedgePop loops per tract were:  8.922946175637394 40.0\n",
      "min,average,max of Home District areas were:  0.0 3.1303999274984475 17.019912520028324\n",
      "calculated statewide vote was 0.494409922719741, should have been 0.49843168320870684\n",
      "calcd statewide Hispanic pop was 0.2661075161047775, should have been 0.2687873422764732\n",
      "calcd statewide Black pop was 0.04290635288945683, should have been 0.04468063376672869\n",
      "fraction of HDs that with altered wedge angles near boundaries =  0.27648725212464587\n"
     ]
    }
   ],
   "source": [
    "#3/7 - 3/10/22 - linear wideAngle, short the opposing wedgePop to match first shorted wedge (see nonper toy)\n",
    "# Bisect for yoyo > 3, precinct calcs after wedges finalized.  More aggressive Euler gain\n",
    "#minTractPop = 10  #this is set in a prior block\n",
    "pi=3.1415926536\n",
    "nWedges = 4  #number of wedges per home district polygon  \n",
    "avgWedgeAngle = 2*pi / nWedges\n",
    "wedgeTriAR = [math.sin(pi/nWedges)*math.cos(pi/nWedges)]*nWedges  #wedge triangle area ratio (= area / r*r)\n",
    "angle = [0.]*nWedges\n",
    "angle2 = [0.]*nWedges\n",
    "pt1 = [Point(0,0)]*nWedges  #these four define the polygon wedge for a growing home district\n",
    "pt2 = [Point(0,0)]*nWedges\n",
    "pt3 = [Point(0,0)]*nWedges\n",
    "pt4 = [Point(0,0)]*nWedges\n",
    "oldR = [0.]*nWedges  #wedge radius from previous loop\n",
    "# guessedR = [0.]*nWedges  #obsolete; was wedge radius if we extrapolate from population density of most recent wedge piece\n",
    "printPoly = [Polygon([(0,0),(0,1),(1,1)])]*nWedges #for debugging\n",
    "wedgePop = [0.]*nWedges\n",
    "tractLoopCounter = [0.]* nTracts  #this tracks how many loops per tract to get to target wedge Pop\n",
    "nearEdge = [0.]* nTracts   #not yet implemented; this will flag tracts near map edge for reprocessing\n",
    "tractUse = [0.] * nTracts  #this will store how much we use this tract in ALL HD's vs. expectation\n",
    "precinctUse = [0.] * nPrecincts   #same, but for each precinct / VTD\n",
    "loopTractUse = [0.] * nTracts  #same as above two, but only for the current loop / tract\n",
    "loopPrecinctUse = [0.] * nPrecincts\n",
    "HDvPop = [0.]*nTracts\n",
    "HDvGOP = [0.]*nTracts  #GOP lean of each tract's \"home district\"\n",
    "HDvBlack = [0.]*nTracts\n",
    "HDvHisp = [0.]*nTracts #pct Hispanic by home district\n",
    "HDweight = [0.]*nTracts  #relative weight of each district.  In absence of splits, will equal precinct pop\n",
    "HDarea = [0.]*nTracts  #geographical area of each home district\n",
    "HDradius = [[0.]*nWedges for t in range(nTracts)] #final wedge length for each Home District wedge\n",
    "HDangle = [[0.]*nWedges for t in range(nTracts)] #final included angle for each HD wedge\n",
    "angle0 = [-999.] * nTracts  #orientation of 0th wedge.  Random except re-oriented if we are near-boundary\n",
    "toler = 0.005  #adjustable - fractional slop in district population.  normally 0.01, to be reduced to 0.005\n",
    "tolerPop = toler * avgDistrictPop  #absolute slop in district pop\n",
    "nLoopPrint = 30  #at this loop number, we alert user of problem\n",
    "nGiveUp = 40  #we punt after this many loops\n",
    "nLoopSuperPrint = nGiveUp - 2 # at this loop number, we output wedge growth visuals\n",
    "wrongPop = [0.]*nTracts\n",
    "normalGain = 0.8  #adjustable - a bit less than 1.0 for stability, how far we step relative to expected perfect guess\n",
    "EulerGain = 1.5 #if we hit empty land, gain more aggressively to get through it\n",
    "#yoyoFactor = 0.3 #rate of reduction in gain as solver continues to yoyo in solving a wedge  #not currently used ***\n",
    "globalMax_dr = (MAP.area/nDistricts) ** 0.5  #put a reasonable upper limit on wedge radial growth step size\n",
    "tractPrintInterval = 20  #for tracking progress\n",
    "minTractArea = 0.00001 * MAP.area / nTracts  #failsafe for later div-by-zero\n",
    "homePopDensity = avgGridDensity  #seed this\n",
    "didWeRestart = [0] * nTracts  #will flag if we adjusted wedge angles due to a single shorted wedge\n",
    "maxAngleRatio = 1.9  #for tightening tract weighting near boundaries  #NEW 3/7/22\n",
    "levelL = 0.36 #sqrt(pop) for angle=std  These two control distortion in wedge angles relative to wedgePop shortness\n",
    "minWedgePop = [avgDistrictPop/nWedges] * nTracts  #wedge pop for wedge facing boundary (for tracts near boundaries)\n",
    "maxWideAngle = 0.8 * pi  #adjustable parameter.  (avoid wedges too close to half a pie\n",
    "\n",
    "for t in range(nTracts) :  #(nTracts):  #loop on each tract.  Start by resetting stats\n",
    "    gain = normalGain  #in case last precinct had convergence problems\n",
    "    if (t % tractPrintInterval) == 0 : \n",
    "        print(\"I am working on tract number {0} of {1} tracts\".format(t,nTracts) )\n",
    "    tractLoopCounter[t] = 0.  #for stability stats, will track how many times we need to loop for each tract\n",
    "    # isActiveG = [0]*nGrids  #resets whether each grid is relevant to this tract (not used; see alt grid turn-on method)\n",
    "    if (tractArea[t] > minTractArea) :  #too lazy to indent everything.  We'll catch zero tractArea later\n",
    "        homePopDensity = tractPop[t]/tractArea[t]  #temporary - estimate the local density for initial step\n",
    "    tractCP = Point(tractGeom[t].centroid.x,tractGeom[t].centroid.y)  #shorthand for centroid of each tract\n",
    "    for nG in range(nGrids):\n",
    "        if gridGeom[nG].contains(tractCP) :  #which grid contains the centroid of this tract?  Turn it on!\n",
    "            # isActiveG[nG] = 1  #I don't think I use this\n",
    "            homeGridDensity = gridDensity[nG]\n",
    "    maxStartDensity = max(homePopDensity,homeGridDensity)\n",
    "    minR = (avgDistrictPop / maxStartDensity / pi)**0.5   # conservative estimated radius of this tract's district\n",
    "    tinyR = 0.01 * minR   #ensures we start with small, but not infinitesimal wedge populations\n",
    "    angle0[t] = random.uniform(0,2.*pi)  #imparts random orientation to our starting wedge\n",
    "    loopTractUse = [0.]*nTracts  #this and below help us reset tract n precinct use after a wedge reset\n",
    "    loopPrecinctUse = [0.]*nPrecincts\n",
    "    # ***GROW WEDGES SIMULTANEOUSLY via ARRAYS, SO WE CAN REACT ON FLY TO BOUNDARY STOPS\n",
    "    HDpop = 0.  #running total of this tract's \"home district\" population\n",
    "    nActiveWedges = nWedges #active wedges have not run over boudnary\n",
    "    targetWedgePop = [avgDistrictPop / nWedges]*nWedges  #at beginning, split districtPop equally among wedges\n",
    "    #latestWedgeDensity = [0.]*nWedges  #stop using # pop'n density of the wedge piece we just added or subtracted\n",
    "    wedgePop = [0.]*nWedges\n",
    "    oldWedgePop = [0.]*nWedges  #wedge pop from previous round (needed for NR projection)\n",
    "    wedgePopGap = [0.]*nWedges\n",
    "    isOverEdge = [0] *nWedges #each wedge is still fully inside map boundary\n",
    "    isSatisfied = [0] * nWedges #wedges close enough to target are not improved upon\n",
    "    yoyoCount = [0] * nWedges #tracks how many times we've reversed this wedge's growth vs. shrink at current wPop target\n",
    "    wedgeMaxR = [0.] * nWedges #will track the largest radius this wedge has seen (to cap yoyo bisection step)\n",
    "    wedgeAngle = [avgWedgeAngle] * nWedges #reset to equi-angle when starting each tract    \n",
    "    currentR = [0.]*nWedges\n",
    "    old_dr = [0.]*nWedges\n",
    "    wedgeStop = 0  #obsolete? this will flag if we need to rejigger oldR when we stopped an over-boundary wedge\n",
    "    for nW in range(nWedges):\n",
    "        currentR[nW] = tinyR  #each wedge starts as a tiny triangle of this radius\n",
    "        old_dr[nW] = currentR[nW] #this will track the last radial step (to help convergence)\n",
    "    oldR = [0.]*nWedges   #for remembering last loop's wedge radius\n",
    "    max_dr = [globalMax_dr]*nWedges  #reset max possible positive radial step size\n",
    "    for nW in range(nWedges) :  #this loop: set up tiny wedges in each direction to seed each wedge loop\n",
    "        angle[nW] = (nW-0.5)*wedgeAngle[nW]+angle0[t]   #local variable for orientation of START of wedge\n",
    "        angle2[nW] = angle[nW]+wedgeAngle[nW]      #local angle for clockwise END of wedge\n",
    "        pt1[nW] = tractCP\n",
    "        pt2[nW] = Point(tractCP.x+currentR[nW]*math.cos(angle[nW]),  tractCP.y+currentR[nW]*math.sin(angle[nW]) )\n",
    "        pt3[nW] = Point(tractCP.x+currentR[nW]*math.cos(angle2[nW]), tractCP.y+currentR[nW]*math.sin(angle2[nW]) )\n",
    "        wedgePoly = Polygon([pt1[nW], pt2[nW], pt3[nW] ]) #build a tiny starter wedge triangle\n",
    "        wedgePop[nW] = tractPop[t]* wedgePoly.area/max(tractArea[t],minTractArea)  #max prevents rare div-by-zero\n",
    "    HDpop = np.sum(wedgePop)\n",
    "    if (tractPop[t] < minTractPop or tractArea[t] == 0 or isSkippedTract[t] == 1):\n",
    "        #go directly to jail, do not pass Go, this tract doesn't count\n",
    "        HDpop = avgDistrictPop  #white lie to kick us out of loop\n",
    "    while abs (HDpop - avgDistrictPop) > tolerPop :  #until we've grown the home district to the right size...\n",
    "        sumWedgePopGapChange = 0  #this will total wedge pops that fall short of expectations (when over boundary)\n",
    "        for nW in range(nWedges) :  #for each wedge, we'll build to gain pop or shrink to lose population ...\n",
    "            neededWedgePop = targetWedgePop[nW] - wedgePop[nW]\n",
    "            isSatisfied[nW] = 0  #default in case target changed last loop.  We'll check immediately below\n",
    "            if abs(neededWedgePop/targetWedgePop[nW]) < 0.5*toler :  #is this wedge close enough to stop iterating on it?\n",
    "                isSatisfied[nW] = 1\n",
    "            if isOverEdge[nW] == 0 and isSatisfied[nW] == 0:   #we skip over-boundary and near-perfect wedges\n",
    "                wedgePopDelta = wedgePop[nW] - oldWedgePop[nW]  #how much wedgePop gained in last loop\n",
    "                if (wedgePopDelta == 0): #our last wedge change was in an empty area (desert or off map)\n",
    "                    gainAdjust = EulerGain/gain  #Euler method often over-cautious at map edges or in sparse areas\n",
    "                    guessedRsquared = currentR[nW]*currentR[nW] * targetWedgePop[nW] / wedgePop[nW]  #Euler guess\n",
    "                    # can't use Newton-Raphson since last dy was zero, use Euler instead\n",
    "                    print(\"we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop\",t,tractLoopCounter[t],\n",
    "                          nW, round(180./pi*wedgeAngle[nW],1),round(currentR[nW],4),round(wedgePop[nW],1))\n",
    "                else: #use N-R estimation\n",
    "                    gainAdjust = 1.0\n",
    "                    R2delta = currentR[nW]*currentR[nW] - oldR[nW]*oldR[nW]  #this and below are run/rise of current slope\n",
    "                    guessedRsquared = currentR[nW]*currentR[nW] + R2delta / wedgePopDelta * neededWedgePop\n",
    "                    \n",
    "                guessedRsquared = max( guessedRsquared, 0. ) #minimum guardrail\n",
    "                guessed_dr = guessedRsquared ** 0.5 - currentR[nW]  #best guess for wedge radius change\n",
    "                #gain = max(0.3,normalGain ** (1. + yoyoFactor * yoyoCount[nW]))  #obsolete; using bisect\n",
    "                dr = gain * gainAdjust * guessed_dr   #gain typically ~0.8 for stability\n",
    "                dr = max( -0.5*currentR[nW],min(dr,max_dr[nW]) )  #apply min and max guardrails to wedge radius change\n",
    "                \n",
    "                if np.sign(dr) != np.sign(old_dr[nW]) :  #are we yoyoing? How many times?\n",
    "                    yoyoCount[nW] += 1\n",
    "                if yoyoCount[nW] > 3 and yoyoCount[nW] <= 6 :\n",
    "                    wedgeMaxR[nW] = max(wedgeMaxR[nW],currentR[nW]) #will track largest R in yoyo cycle\n",
    "                    max_dr[nW] = 2.*wedgeMaxR[nW]  #this will now store a reasonable max bisection step size\n",
    "                if yoyoCount[nW] > 6 :  #switch to bisection method; too many yo-yo's\n",
    "                    print(yoyoCount[nW],\"yoyos for tract,wedge,wedgePop,r=\",t,nW,wedgePop[nW],round(currentR[nW],4) )\n",
    "                    max_dr[nW] = 0.5 * max_dr[nW] #we are now bisecting ...\n",
    "                    if(neededWedgePop < 0):\n",
    "                        dr = max(-1.*max_dr[nW], -0.5*currentR[nW])  #reduce wedge size\n",
    "                    else:\n",
    "                        dr = max_dr[nW]  #increase wedge size\n",
    "                old_dr[nW] = dr  #these three lines -- save current as old values for next loop\n",
    "                oldR[nW] = currentR[nW]\n",
    "                oldWedgePop[nW] = wedgePop[nW]\n",
    "                if dr > 0. :  #we are growing a wedge trapezoid piece \n",
    "                    outerR = currentR[nW] + dr\n",
    "                    innerR = currentR[nW]\n",
    "                    currentR[nW] = outerR    #for next loop around\n",
    "                else:    #this wedge trapezoid piece will be SUBTRACTED from current wedge\n",
    "                    outerR = currentR[nW]\n",
    "                    innerR = currentR[nW] + dr  #remember, dr is negative here\n",
    "                    currentR[nW] = innerR             #for next loop around\n",
    "                #now, describe the new wedge to probe for precinct intersections ...\n",
    "                pt1[nW] = Point(tractCP.x+innerR*math.cos(angle[nW]), tractCP.y+innerR*math.sin(angle[nW]) )\n",
    "                pt2[nW] = Point(tractCP.x+outerR*math.cos(angle[nW]), tractCP.y+outerR*math.sin(angle[nW]) )\n",
    "                pt3[nW] = Point(tractCP.x+outerR*math.cos(angle2[nW]),tractCP.y+outerR*math.sin(angle2[nW]) )\n",
    "                pt4[nW] = Point(tractCP.x+innerR*math.cos(angle2[nW]),tractCP.y+innerR*math.sin(angle2[nW]) )\n",
    "                wedgePoly = Polygon([pt1[nW], pt2[nW], pt3[nW],pt4[nW]])  #true for wedge add-on or to-be-trimmed\n",
    "                \n",
    "                printPoly[nW] = wedgePoly  #for debugging\n",
    "                latestWedgePop = 0.  #for the new piece, not the entire triangle\n",
    "                usedTract = [0]*nTracts  #rezero lists of tracts and precincts that could straddle multiple grids\n",
    "                usedPrecinct = [0]*nPrecincts\n",
    "                for nG in range(nGrids) :  # loop over ACTIVE grids to look for intersecting tracts\n",
    "                    gridIntersxn = gridGeom[nG].intersection(wedgePoly)\n",
    "                    if (gridIntersxn.area > 0) :  #this grid is RELEVANT to this wedge\n",
    "                        for tt in range(nGridTracts[nG]) : #look for intersxns with all tracts in this grid\n",
    "                            nTT = gridTractNo[nG][tt] #shorthand for this tract's global tract no\n",
    "                            if(usedTract[nTT] == 0) :  #Did we not already look at this tract in another grid list?\n",
    "                                usedTract[nTT] = 1  #well, now we have!  Probe intersection with wedge\n",
    "                                overlap = tractGeom[nTT].intersection(wedgePoly).area\n",
    "                                if overlap > 0 :\n",
    "                                    fracArea = overlap/tractArea[nTT]\n",
    "                                    latestWedgePop  += fracArea*tractPop[nTT]  #always positive (used in density calc)\n",
    "                                    loopTractUse[nTT] += np.sign(dr)*overlap/tractArea[nTT] * tractPop[t]/avgDistrictPop\n",
    "                        # found all possible tract overlaps with this increment / decrement to this wedge.\n",
    "                        \n",
    "                wedgePop[nW] += np.sign(dr)*latestWedgePop  #for full triangle, based on this latest piece\n",
    "                # Now, flag if we're beyond boundary\n",
    "                if wedgePop[nW] < targetWedgePop[nW] : #if growing, confirm we're not beyond MAP boundary\n",
    "                    leadingEdge = LineString([pt2[nW],pt3[nW]])  #leading edge of growing wedgePoly\n",
    "                    fracArea = wedgePoly.intersection(MAP).area / max(0.00000001,wedgePoly.area) #what pct of new wedge piece on map?\n",
    "                    if leadingEdge.intersects(MAP) and fracArea > 0.05 :  #still room to grow in part of edge...\n",
    "                        conclusion = \"keep going\"  #** NEW 3/23/22 - add fracArea criterion on top of leadingEdge criterion *****\n",
    "                    else: #conclude this wedge is fully beyond the map. give up on more map intersection\n",
    "                        isOverEdge[nW] = 1\n",
    "                        shortedWedge = nW  #ID'ing highest numbered wedge that got shorted by the boundary in this loop\n",
    "                        oldWedgePopGap = wedgePopGap[nW]\n",
    "                        wedgePopGap[nW] = targetWedgePop[nW] - wedgePop[nW]  #how far this wedge's pop is below its target\n",
    "                        nActiveWedges -= 1  #a few lines below, we will adjust other wedge's targets\n",
    "                        sumWedgePopGapChange += wedgePopGap[nW] - oldWedgePopGap\n",
    "                        if (nActiveWedges == 0) : #we're doomed, somehow all wedges are short and off map\n",
    "                            print(\"PUNT! no more active wedges for tract, loop =\",t,tractLoopCounter[t] )\n",
    "                            tractLoopCounter[t] = nGiveUp + 1  #PUNT !!!\n",
    "                            \n",
    "                        max_dr = [globalMax_dr]*nWedges   #allow other wedges to take big steps again to catch up\n",
    "                        yoyoCount = [0] * nWedges  #go back to original gains on ALL active wedges\n",
    "                   \n",
    "        # end of nW loop to adjust all wedge populations by growing or trimming wedges, IDing over-edge wedges\n",
    "        tractLoopCounter[t] +=1   # still looping on home district Pop.\n",
    "        nReceivingWedges = nActiveWedges\n",
    "        oppFlag = 0\n",
    "        if targetWedgePop[int(nWedges/2)] < 0.99 * avgDistrictPop/float(nWedges): #is opposite wedge restricted?\n",
    "            oppFlag = 1\n",
    "            nReceivingWedges -= 1-isOverEdge[int(nWedges/2)]  #if yes, don't count opposite wedge as a receiver\n",
    "        if nReceivingWedges == 0: #rarely, the opposite wedge is the only active one\n",
    "            for nWW in range(nWedges): #in this case, we allow the opposite wedge to pick up the slack\n",
    "                targetWedgePop[nWW] += sumWedgePopGapChange/max(1.,float(nReceivingWedges) )\n",
    "        else: #if the opp wedge has a ceiling wedgePopTarget, distribute wedgePopGap to other active wedges\n",
    "            for nWW in range(nWedges):  #time to make adjustments in target wedge pops based on boundary fails\n",
    "                if nWW != int(nWedges/2) or oppFlag == 0 :  #excluding opposite wedge as a receiver\n",
    "                    targetWedgePop[nWW] += sumWedgePopGapChange/float(nReceivingWedges)\n",
    "\n",
    "        # *** NEW 1/19/22 CODE TO ADJUST WEDGE ANGLES WHEN A BOUNDARY ENCOUNTERED\n",
    "        if (nActiveWedges == nWedges or nActiveWedges < nWedges - 2\n",
    "           or didWeRestart[t] == 1) :  #0 or 3+ over-boundary short wedges, or we've already adjusted wedge angles once\n",
    "            HDpop = np.sum(wedgePop) #Keep going with normal wedge growth and trim process\n",
    "        else :  # 1 OR 2 SHORTED WEDGES.  MAY WANT TO ALTER WEDGE ANGLES\n",
    "            didWeRestart[t] = 1  #to ensure we adjust wedge angles and target wedgePops at most once per tract\n",
    "            if (nActiveWedges == nWedges - 2) :  #exactly two wedges got shorted in this loop -- are they opposing?\n",
    "                oppW = int( (shortedWedge + nWedges/2) % nWedges)  #the index of wedge opposite the last shorted wedge\n",
    "                if (isOverEdge[oppW] == 0) :\n",
    "                    print(\"we have 2 non-opposing shorted wedges for tract no\",t)\n",
    "                    totalLiveAngle = 2.*pi - np.sum(isOverEdge)*avgWedgeAngle  #avail angle to divvy among live wedges  \n",
    "                    for nW in range (nWedges) : #Decrease wedge angles opposite shorted wedges via a complex weighting\n",
    "                        if isOverEdge[nW] == 0 :\n",
    "                            oppW = int( (nW + nWedges/2) % nWedges) \n",
    "                            angleWeight = (np.sum(wedgePopGap)-wedgePopGap[oppW])/np.sum(wedgePopGap)/(nActiveWedges-1)\n",
    "                            wedgeAngle[nW] = angleWeight * totalLiveAngle  \n",
    "                else : #shorted wedges ARE opposing\n",
    "                    print(\"we have 2 OPPOSING shorted wedges for tract no\",t) # no wedge-angle adjustment in this case\n",
    "                    #wedgeAngle=[avgWedgeAngle]*nWedges  #comment this out, no change\n",
    "            else: # a single shorted wedge (over boundary).  COMPLETELY RESTART wedge growth process\n",
    "                shortW = shortedWedge #Next dozen lines: convoluted code to find angle to closest boundary point\n",
    "                #print(\"1 wedge over map boundary for tract\",t,\"at loop, wedgePop\",tractLoopCounter[t],wedgePop[shortW] )\n",
    "                shortedPoly = Polygon([tractCP, pt2[shortW],pt3[shortW] ])\n",
    "                MAPedge = shortedPoly.intersection(MAP.exterior) #true state boundary line where wedge crossed it\n",
    "                minDistance = max(tractCP.distance(MAPedge),tinyR)  #closest distance to edge where wedge hit the boundary\n",
    "                edgeCircle = tractCP.buffer(1.01*minDistance)  #build a circle a little bigger than this distance\n",
    "                #  1.001 is not big enough; will occasionally not intersect\n",
    "                closeArea = edgeCircle.intersection(MAPedge)\n",
    "                counter = 0.\n",
    "                maxCounter = 10. #give up after 10 tries\n",
    "                while closeArea.is_empty :  # protect against missing the map boundary somehow\n",
    "                    print(\"need to widen edge circle beyond\",minDistance, \"for tract (x,y)=(\",tractCP.x,tractCP.y)\n",
    "                    minDistance = minDistance * 1.1\n",
    "                    edgeCircle = tractCP.buffer(minDistance)  #widen the circle\n",
    "                    closeArea = edgeCircle.intersection(MAPedge)\n",
    "                    counter += 1\n",
    "                    if counter >= maxCounter: #not finding map edge intersection.  Try another way\n",
    "                        print(\"did not find boundary with circle approach.  Brute-force it.\")\n",
    "                        minDistance = tractCP.distance(MAP.exterior)\n",
    "                        edgeCircle = tractCP.buffer(1.01*minDistance)\n",
    "                        closeArea = edgeCircle.intersection(MAP.exterior)                        \n",
    "            \n",
    "                closePoint = closeArea.centroid  # this is a point very close to the closest beeline from tract to map edge\n",
    "                #print(closePoint,tractCP, \"are boundary point and tract center point\")\n",
    "                x1 = closePoint.x\n",
    "                x2 = tractCP.x  #debugging\n",
    "                dx = closePoint.x - tractCP.x\n",
    "                dy = closePoint.y - tractCP.y\n",
    "                exitAngle = pi/2. * np.sign(dy)  #default in case dx=0\n",
    "                if (dx != 0 ):\n",
    "                    exitAngle = math.atan(dy/dx)  #this is the angle from the x-axis to the exit beeline\n",
    "                    if dx < 0 :  #use complementary atan solution; boundary is west of tract centroid\n",
    "                        exitAngle = pi + exitAngle\n",
    "                angle0[t] = exitAngle # this reorients 0th wedge to face boundary's closest point               \n",
    "                # New 1/23/22 - let's estimate wedgePopGap at ideal orientation, normal angle   # ****************\n",
    "                wedgeAngle1 = exitAngle - 0.5*avgWedgeAngle\n",
    "                wedgeAngle2 = exitAngle + 0.5*avgWedgeAngle\n",
    "                maxR = max(stateWidth,stateHeight) #ensuring we make a big enough wedge\n",
    "                wedgePt1 = tractCP\n",
    "                wedgePt2 = Point(tractCP.x+maxR*math.cos(wedgeAngle1),  tractCP.y+maxR*math.sin(wedgeAngle1) )\n",
    "                wedgePt3 = Point(tractCP.x+maxR*math.cos(wedgeAngle2), tractCP.y+maxR*math.sin(wedgeAngle2) )\n",
    "                wedgePoly = Polygon([ wedgePt1, wedgePt2, wedgePt3 ])\n",
    "                minWedgePop[t] = 0. #the wedgePop of the shorted wedge if we re-orient but don't adjust angles\n",
    "                usedTract = [0]*nTracts  #rezero lists of tracts and precincts that could straddle multiple grids\n",
    "                for nG in range(nGrids) :  # loop over ACTIVE grids to look for intersecting tracts\n",
    "                    gridIntersxn = gridGeom[nG].intersection(wedgePoly)\n",
    "                    if (gridIntersxn.area > 0) :  #this grid is RELEVANT to this wedge\n",
    "                        for tt in range(nGridTracts[nG]) : #look for intersxns with all tracts in this grid\n",
    "                            nTT = gridTractNo[nG][tt] #shorthand for this tract's global tract no\n",
    "                            if(usedTract[nTT] == 0) :  #Did we not already look at this tract in another grid list?\n",
    "                                usedTract[nTT] = 1  #well, now we have!  Probe intersection with wedge\n",
    "                                overlap = tractGeom[nTT].intersection(wedgePoly).area\n",
    "                                if overlap > 0 :\n",
    "                                    fracArea = overlap/tractArea[nTT]\n",
    "                                    minWedgePop[t]  += fracArea*tractPop[nTT]  #always positive (used in density calc)\n",
    "                #print(\"the minimum shorted Wedge Pop for tract {0} is {1}\".format(t,minWedgePop[t]) )  #debug\n",
    "                #printAngle[t] = round(exitAngle*180./pi,4) #for debugging\n",
    "                printDist = round(minDistance,4)\n",
    "                #print(t,\"(\",tractCP.x,tractCP.y,\")\",printAngle,printDist,\"t,tract(x,y),exitAngle,dist\")\n",
    "                # ********************************************************************\n",
    "                # printWedgePopGap[t] = wedgePopGap[shortW]\n",
    "                #now, we \"squish the square\" based on how close we are to boundary - see offline documentation\n",
    "                equivL = (4.*minWedgePop[t] / avgDistrictPop)**0.5  #non-dimensional centroid-boundary distance\n",
    "                wideAngle = avgWedgeAngle*max(  1,( 1.+(maxAngleRatio-1.)*(levelL - equivL)/(levelL - 0.) )  )\n",
    "                wideAngle = min(maxWideAngle, wideAngle)  #prevent too wide of an angle (half-pie)\n",
    "                #HERE, WE ADJUST all wedge ANGLES\n",
    "                wideAngle = min(wideAngle, maxWideAngle*pi) #set an upper limit to avoid gaping wedges\n",
    "                thinAngle = (2.*pi - 2.*wideAngle)/(nWedges - 2.)  #other wedges equally angle-compressed\n",
    "                for nW in range(nWedges) :\n",
    "                    if (nW == 0 or nW == int(nWedges/2) ) : #assign these two as shorted wedge and its opposite\n",
    "                        wedgeAngle[nW] = wideAngle\n",
    "                    else :\n",
    "                        wedgeAngle[nW] = thinAngle\n",
    "                #** COMPLETELY RESTART WEDGE GROWTH PROCESS FROM INITIAL PIZZA SLICES, NOW RE-ORIENTED\n",
    "                loopTractUse = [0.] * nTracts  # reset as we are starting over\n",
    "                loopPrecinctUse = [0.]* nPrecincts\n",
    "                HDpop = 0.\n",
    "                nActiveWedges = nWedges\n",
    "                # NEW 3/10/22 - we not only squish the angles, we short the target for the wedge opposite boundary\n",
    "                oppWedgeExpectedPop = minWedgePop[t] * math.tan(0.5*wideAngle)/math.tan(pi/nWedges)  #prediction for new wedge0's pop\n",
    "                oppWedgeExpectedPop = min(oppWedgeExpectedPop,avgDistrictPop/nWedges) #not needed, but just in case...\n",
    "                for nW in range(nWedges):\n",
    "                    if nW == int(nWedges/2):\n",
    "                        targetWedgePop[nW] = oppWedgeExpectedPop\n",
    "                    else:  #NOTE: we don't mess w wedge0's target.  When it shorts again, we will adjust other wedge's tgts\n",
    "                        targetWedgePop[nW] =(avgDistrictPop - oppWedgeExpectedPop)/float(nWedges-1)\n",
    "                # *** END OF 3/10/22 MODIFICATION\n",
    "                oldR = [0.]*nWedges   #for remembering last loop's wedge radius  \n",
    "                currentR = [tinyR]*nWedges                \n",
    "                old_dr = [tinyR]*nWedges  #this will track the last radial step (to help convergence)\n",
    "                max_dr = [globalMax_dr]*nWedges  #reset max step size\n",
    "                wedgePop = [0.]*nWedges\n",
    "                oldWedgePop = [0.]*nWedges #wedge pop from previous round\n",
    "                wedgePopGap = [0.]*nWedges\n",
    "                isOverEdge = [0] *nWedges\n",
    "                isSatisfied = [0] *nWedges\n",
    "                yoyoCount = [0] *nWedges #just in case we missed this earlier\n",
    "                wedgeMaxR = [0.] * nWedges #resetting\n",
    "                angle[0] = angle0[t] - 0.5*wideAngle  #the 0th wedge always faces the boundary\n",
    "                angle2[0] = angle[0] + wedgeAngle[0]\n",
    "                for nW in range(nWedges) :\n",
    "                    if (nW == 0):\n",
    "                        angle[nW] = angle0[t] - 0.5*wideAngle  #we have to start somewhere :-)\n",
    "                    else :\n",
    "                        angle[nW] = angle2[nW-1]\n",
    "                    angle2[nW] = angle[nW]+wedgeAngle[nW]      \n",
    "                    pt1[nW] = tractCP\n",
    "                    pt2[nW] = Point(tractCP.x+tinyR*math.cos(angle[nW]),  tractCP.y+tinyR*math.sin(angle[nW]) )\n",
    "                    pt3[nW] = Point(tractCP.x+tinyR*math.cos(angle2[nW]), tractCP.y+tinyR*math.sin(angle2[nW]) )\n",
    "                    wedgePoly = Polygon([pt1[nW], pt2[nW], pt3[nW] ])\n",
    "                    wedgePop[nW] = tractPop[t]* wedgePoly.area/max(tractArea[t],minTractArea)\n",
    "                HDpop = np.sum(wedgePop)   #** END OF RESTART BLOCK.  RE-ENTER MAIN LOOP FOR THIS TRACT\n",
    "                      \n",
    "        # **** BELOW IS TO CORRECT FOR NONCONVERGENCE *****\n",
    "        if (tractLoopCounter[t] > nLoopPrint):  #may be becoming unstable. Alert user,    #and (don't) reduce gain\n",
    "            strPop = str(round(HDpop,2))\n",
    "            strWdg = \"Overedge?\"\n",
    "            strSat = \"Satisfied?\"\n",
    "            strYoyo = \"yoyo?\"                \n",
    "            strTWP_dr = \"tWP,dr\"\n",
    "            for nWW in range(nWedges) :\n",
    "                strPop = strPop + \", \"+str(round(wedgePop[nWW],1) )\n",
    "                strWdg = strWdg +str(isOverEdge[nWW])+\", \"\n",
    "                strSat = strSat +str(isSatisfied[nWW])\n",
    "                strYoyo = strYoyo + str(yoyoCount[nWW])\n",
    "                strTWP_dr = strTWP_dr + \", \"+str(round(targetWedgePop[nWW],1) )+\",\"+str(round(old_dr[nWW],4) )\n",
    "            print(\"loop{0}, tr{1},wedgePops{2}, {3},{4},{5} \".format(tractLoopCounter[t],t,strPop,strWdg,strSat,strYoyo) )\n",
    "            print(\"   targetWP, latest drx4 are\",strTWP_dr)\n",
    "\n",
    "        if (tractLoopCounter[t] > nLoopSuperPrint) :\n",
    "            for nWW in range(nWedges):                \n",
    "                print(\"wedge no, currentR,oldWedgePop, wedgePop, overEdge?\",nWW,str(round(currentR[nWW],5)), \n",
    "                      str(round(oldWedgePop[nWW],4)), str(round(wedgePop[nWW],4)),isOverEdge[nWW])   #debug\n",
    "                x, y = printPoly[nWW].exterior.xy     #wedge debugging .....\n",
    "                plt.plot(x, y, c=(0.1, 0.2, 0.02+float(nWW)/nWedges) )\n",
    "            plt.show()\n",
    "        if(tractLoopCounter[t] >= nGiveUp):\n",
    "            print(\"I looped {0} times on tract {1}, giving up w pop {2}\".format(nGiveUp,t,HDpop) )\n",
    "            wrongPop[t] = HDpop  #this will flag this HD as triaged\n",
    "            HDpop = avgDistrictPop  #white lie to kick out of loop\n",
    "        # *** END OF TRIAGE FOR NONCONVERGENCE\n",
    "        \n",
    "    # END OF WHILE LOOP --> we are within tolerance of avgDistrictPop. Finalize this tract's Home District stats\n",
    "    for nTT in range (nTracts) :\n",
    "        tractUse[nTT] += loopTractUse[nTT]\n",
    "    totGOP = 0.\n",
    "    totVote = 0.\n",
    "    totVAP = 0.\n",
    "    totHisp = 0.\n",
    "    totBlack = 0.  #zero these out prior to summing over final wedges\n",
    "    usedTract = [0]*nTracts  #rezero lists of tracts and precincts that could straddle multiple grids\n",
    "    usedPrecinct = [0]*nPrecincts\n",
    "\n",
    "    if (tractPop[t] < minTractPop or tractArea[t] == 0 or isSkippedTract[t] == 1): \n",
    "        #we bypassed the big loop; this tract is inconsequential\n",
    "        HDvPop[t] = HDpop  #a white lie\n",
    "        HDweight[t] = 0.000001  #to suppress this in total stats\n",
    "    else :\n",
    "        HDvPop[t] = np.sum(wedgePop)\n",
    "        # HDvHisp[t] = np.sum(wedgeHisp)/np.sum(wedgePop)  #3/2/22 - move to final wedge calcs\n",
    "        # HDvBlack[t] = np.sum(wedgeBlack)/np.sum(wedgePop)  #3/2/22 - move to final wedge calcs\n",
    "        HDweight[t] = tractPop[t]/np.sum(tractPop)\n",
    "        nearEdge[t] = nWedges - nActiveWedges  #flagging the number of wedges that were not completely pop-filled\n",
    "        centerPt = tractCP\n",
    "        outerPt2 = Point(tractCP.x+currentR[0]*math.cos(angle[0]), tractCP.y+currentR[0]*math.sin(angle[0]) )\n",
    "        outerPt3 = Point(tractCP.x+currentR[0]*math.cos(angle2[0]),tractCP.y+currentR[0]*math.sin(angle2[0]) )\n",
    "        HDpolly = Polygon([centerPt,outerPt2,outerPt3])   #initiate a polygon that will be the home district\n",
    "        for nW in range(nWedges):\n",
    "            HDradius[t][nW] = currentR[nW]\n",
    "            HDangle[t][nW] = angle[nW]\n",
    "            \n",
    "        for nW in range(1,nWedges):  #save final geometry, compute minority and partisan stats\n",
    "            cR = currentR[nW] #shorthand\n",
    "            outerPt2 = Point(tractCP.x+cR*math.cos(angle[nW]), tractCP.y+cR*math.sin(angle[nW]) )\n",
    "            outerPt3 = Point(tractCP.x+cR*math.cos(angle2[nW]),tractCP.y+cR*math.sin(angle2[nW]) )\n",
    "            HDpolly =  HDpolly.union( Polygon([centerPt,outerPt2,outerPt3]) )  #add this wedge to HD district polygon\n",
    "        HDpolly = HDpolly.intersection(MAP)  #exclude map's convex hull and buffer, just the original union of precincts\n",
    "        HDarea[t] = HDpolly.area  #for stats, final Home District area\n",
    "        for nG in range(nGrids) :  # ID the ACTIVE grids to look for intersecting precincts\n",
    "            gridIntersxn = gridGeom[nG].intersection(HDpolly)\n",
    "            if (gridIntersxn.area > 0) :  #this grid is RELEVANT to the final Home District\n",
    "                for tt in range(nGridTracts[nG]):\n",
    "                    nTT = gridTractNo[nG][tt]\n",
    "                    if usedTract[nTT] == 0: #only examine tracts that have not already been called for intersection\n",
    "                        usedTract[nTT] == 1  #this tract has now been called \n",
    "                        overlap = tractGeom[nTT].intersection(HDpolly).area\n",
    "                        if overlap > 0 :\n",
    "                            fracArea = overlap/tractArea[nTT]\n",
    "                            totVAP += fracArea*tractVAP[nTT]\n",
    "                            totHisp += fracArea*tractHisp[nTT]\n",
    "                            totBlack += fracArea*tractBlack[nTT]\n",
    "                        \n",
    "                for pp in range(nGridPrecincts[nG]): #scanning the list of precincts in this active grid\n",
    "                    nPP = gridPrecinctNo[nG][pp]\n",
    "                    if usedPrecinct[nPP] == 0  :\n",
    "                        usedPrecinct[nPP] = 1  #don't double up on precinct intersection\n",
    "                        overlap = vtdGeom[nPP].intersection(HDpolly).area\n",
    "                        if overlap > 0 :\n",
    "                            fracArea = overlap/vtdArea[nPP]\n",
    "                            totGOP += fracArea*vtdGOP[nPP]*vtdPop[nPP]\n",
    "                            totVote += fracArea*vtdPop[nPP]\n",
    "                            loopPrecinctUse[nPP] += overlap/vtdArea[nPP] *tractPop[t]/avgDistrictPop\n",
    "        HDvHisp[t] = totHisp/totVAP\n",
    "        HDvBlack[t] = totBlack/totVAP\n",
    "        HDvGOP[t] = totGOP / totVote\n",
    "        for nPP in range (nPrecincts) :\n",
    "            precinctUse[nPP] += loopPrecinctUse[nPP] #add to global use for this precinct\n",
    "            \n",
    "        \n",
    "\n",
    "    # end of loop on this tract\n",
    "for t in range(nTracts):\n",
    "    if(wrongPop[t] > 0):\n",
    "        HDvPop[t] = wrongPop[t]  #undo the lie that got us out of the loop early\n",
    "HDsumWeight = np.sum(HDweight)\n",
    "print(\"Sum of weights over all precinct home districts should have been 1.000, but was \",HDsumWeight)\n",
    "print(\"Average and max number of wedgePop loops per tract were: \",np.average(tractLoopCounter),np.max(tractLoopCounter) )\n",
    "print(\"min,average,max of Home District areas were: \",np.min(HDarea),np.average(HDarea),np.max(HDarea) )\n",
    "stateGOP2 = 0.\n",
    "stateHisp2 = 0.\n",
    "stateBlack2 = 0.\n",
    "for t in range(nTracts):\n",
    "    HDweight[t] = HDweight[t]/HDsumWeight   #renormalizing\n",
    "    stateGOP2 += HDweight[t]*HDvGOP[t]\n",
    "    stateBlack2 += HDweight[t]*HDvBlack[t]\n",
    "    stateHisp2 += HDweight[t]*HDvHisp[t]\n",
    "statePop = np.sum(tractPop)\n",
    "print(\"calculated statewide vote was {0}, should have been {1}\".format(stateGOP2, stateGOP) )\n",
    "print(\"calcd statewide Hispanic pop was {0}, should have been {1}\".format(stateHisp2, np.sum(tractHisp)/stateVAP ) )\n",
    "print(\"calcd statewide Black pop was {0}, should have been {1}\".format(stateBlack2, np.sum(tractBlack)/stateVAP ) )\n",
    "print(\"fraction of HDs that with altered wedge angles near boundaries = \",np.sum(didWeRestart)/nTracts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "24a347a9-8220-4c1a-bcfc-14923f1f98f7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sum of weights over all precinct home districs should have been 1.000, but was  1.0001088050758582\n",
      "Average and max number of wedgePop loops per tract were:  8.922946175637394 40.0\n",
      "min,average,max of Home District areas were:  0.0 3.1303999274984475 17.019912520028324 for  AZ\n"
     ]
    }
   ],
   "source": [
    "#use this line to document progress\n",
    "# start AZ 7:30am, end 8:35am (with some other work going on)\n",
    "print(\"Sum of weights over all precinct home districs should have been 1.000, but was \",HDsumWeight)\n",
    "print(\"Average and max number of wedgePop loops per tract were: \",np.average(tractLoopCounter),np.max(tractLoopCounter) )\n",
    "print(\"min,average,max of Home District areas were: \",np.min(HDarea),np.average(HDarea),np.max(HDarea),\"for \",STATE )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "72247942-899a-4ab2-9796-239aa1e524fb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AZ 25Mar\n"
     ]
    }
   ],
   "source": [
    "date = \"25Mar\"\n",
    "print(STATE, date)  #check that I will not overwrite existing file :-)\n",
    "tractCPx = [0.]*nTracts\n",
    "tractCPy = [0.]*nTracts\n",
    "tractNo = [0.]*nTracts\n",
    "for t in range(nTracts):\n",
    "    tractCPx[t]=tractGeom[t].centroid.x\n",
    "    tractCPy[t]=tractGeom[t].centroid.y\n",
    "    tractNo[t]=t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "c55ac5fd-7402-47b9-9093-10b9fe0912b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "#LET'S WRITE AN OUTPUT FILE BEFORE WE FORGET :-)\n",
    "#convert HD wedge geometries to 1D arrays.  BELOW IS FOR 4-WEDGE.  CAN AUGMENT FOR 6-WEDGE\n",
    "HDangle0 = [0.]*nTracts\n",
    "HDradius0 = [0.]*nTracts\n",
    "HDangle1 = [0.]*nTracts\n",
    "HDradius1 = [0.]*nTracts\n",
    "HDangle2 = [0.]*nTracts\n",
    "HDradius2 = [0.]*nTracts\n",
    "HDangle3 = [0.]*nTracts\n",
    "HDradius3 = [0.]*nTracts\n",
    "for t in range(nTracts):\n",
    "    HDangle0[t] = HDangle[t][0]\n",
    "    HDradius0[t] = HDradius[t][0]\n",
    "    HDangle1[t] = HDangle[t][1]\n",
    "    HDradius1[t] = HDradius[t][1]\n",
    "    HDangle2[t] = HDangle[t][2]\n",
    "    HDradius2[t] = HDradius[t][2]\n",
    "    HDangle3[t] = HDangle[t][3]\n",
    "    HDradius3[t] = HDradius[t][3]\n",
    "# now convert output to pandas dataframe and export\n",
    "\n",
    "paramList = [\"STATE\",\"stateGOP\",\"nDistricts\",\"nTracts\",\"nPrecincts\",\"nWedges\",\"popn-toler\",\n",
    "             \"gain\",\"maxWideAngle\",\"levelL\"]\n",
    "paramValues = [STATE,stateGOP, nDistricts, nTracts,nPrecincts,nWedges, toler, gain, maxWideAngle, levelL]\n",
    "for i in range(nTracts-len(paramList)):\n",
    "    paramList.append(\".\")\n",
    "    paramValues.append(-99)  #so all columns have same number of entries, even the parameter list\n",
    "df = pd.DataFrame( {\"paramList\": paramList,\"paramValues\":paramValues,\"HD-pop\":HDvPop,\"HDvGOP\":HDvGOP,\"HDvHisp\":HDvHisp,\n",
    "                    \"HDvBlack\":HDvBlack,\"HDwt\":HDweight,\"HDarea\":HDarea, \"HDangle0\":HDangle0, \"HDangle1\":HDangle1,\n",
    "                    \"HDangle2\":HDangle2,\"HDangle3\":HDangle3,\"HDradius0\":HDradius0,\"HDradius1\":HDradius1,\n",
    "                    \"HDradius2\":HDradius2,\"HDradius3\":HDradius3,\"startAngle\":angle0,\"tractNo\":tractNo,\n",
    "                    \"Loops\":tractLoopCounter,\"tractPop\":tractPop,\"tractHisp\":tractHisp,\"tractBlack\":tractBlack,\n",
    "                    \"centroid x\":tractCPx,\"centroid y\":tractCPy, \"tractUse\":tractUse,\"nearEdge\":nearEdge,\n",
    "                    \"wrongPop\":wrongPop,\"Restart?\":didWeRestart} ) \n",
    "\n",
    "outname = STATE+str(nDistricts)+\"HD1tol\"+str(toler)+\"nW\"+str(nWedges)+date+\".csv\"\n",
    "outpath = \"state_HD_output/\"+outname\n",
    "df.to_csv(outpath)\n",
    "precinctNo = [0.]*nPrecincts\n",
    "vtdX = [0.]*nPrecincts\n",
    "vtdY = [0.]*nPrecincts\n",
    "for p in range(nPrecincts):\n",
    "    precinctNo[p]=p\n",
    "    vtdX[p] = vtdGeom[p].centroid.x\n",
    "    vtdY[p] = vtdGeom[p].centroid.y\n",
    "df2 = pd.DataFrame( {\"precinctNo\":precinctNo,\"precinctPop\":vtdPop,\"precUse\":precinctUse, \"vtdX\":vtdX, \"vtdY\":vtdY} )\n",
    "outname2 = STATE+date+\"_VTD_tol\"+str(toler)+\"nW\"+str(nWedges)+\".csv\"\n",
    "outpath = \"state_HD_output/\"+outname2\n",
    "df2.to_csv(outpath)  #currently, I'm outputting the precinct use stats to a separate file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "c4449da0-47a9-40cc-a585-8288447290e6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "196 1.01 ( -111.06387953868692 32.30870839932819 ) 0.4462668033980133 t, pop/target,(x,y), pctR\n",
      "853 1.01 ( -111.12743070999146 32.351111796553596 ) 0.47729663990332843 t, pop/target,(x,y), pctR\n",
      "1077 1.01 ( -112.12585931820504 33.42600865200607 ) 0.29778194218544213 t, pop/target,(x,y), pctR\n",
      "1348 1.03 ( -112.71967611773432 33.687588482558496 ) 0.6415029849704512 t, pop/target,(x,y), pctR\n",
      "1593 1.02 ( -113.14541859263183 34.57374805260021 ) 0.5722635057996128 t, pop/target,(x,y), pctR\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "we will redo tract numbers  [0]\n"
     ]
    }
   ],
   "source": [
    "# Looking for nonconvergers ...  Run even if none observed, just to check  \n",
    "redo = [0]\n",
    "counter = 0.\n",
    "for t in range (nTracts) :\n",
    "    if wrongPop[t] > 0 :\n",
    "        ratio = round(wrongPop[t]/avgDistrictPop,2)\n",
    "        tractX = tractGeom[t].centroid.x\n",
    "        tractY = tractGeom[t].centroid.y        \n",
    "        print(t, ratio,\"(\",tractX,tractY,\")\",HDvGOP[t], \"t, pop/target,(x,y), pctR\")\n",
    "        if (ratio < 0.9 or ratio > 1.1) :  #these we will redo\n",
    "            plt.text(tractGeom[t].centroid.x, tractGeom[t].centroid.y,ratio, fontsize=14)\n",
    "            if counter == 0 :  #first redo\n",
    "                redo = [t]\n",
    "            else :\n",
    "                redo.append(t)\n",
    "            counter+= 1\n",
    "        else: #show these milder offenders on the map in a smaller font            \n",
    "            plt.text(tractGeom[t].centroid.x, tractGeom[t].centroid.y,ratio, fontsize=9)\n",
    "\n",
    "x,y = tractMAP.exterior.xy\n",
    "plt.plot(x,y,c=\"purple\")\n",
    "plt.show()\n",
    "print(\"we will redo tract numbers \",redo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "be6e965b-4ca8-4af2-8031-63132fbb939d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6 0.6731027553428781 650 pop,GOP 0.13846153846153847\n",
      "15 0.651469399816353 992 pop,GOP 0.10383064516129033\n",
      "19 0.6501780489536639 164 pop,GOP 0.17682926829268292\n",
      "23 0.6432110260980116 484 pop,GOP 0.3450413223140496\n",
      "24 0.68396401317626 913 pop,GOP 0.12157721796276014\n",
      "26 0.6687875477240803 627 pop,GOP 0.09728867623604466\n",
      "29 0.6706529417607769 565 pop,GOP 0.1504424778761062\n",
      "30 0.6545937966287089 388 pop,GOP 0.1958762886597938\n",
      "31 0.64400726774763 537 pop,GOP 0.18808193668528864\n",
      "33 0.6562069838112872 1067 pop,GOP 0.07403936269915651\n",
      "38 0.6679677478257283 1021 pop,GOP 0.3721841332027424\n",
      "39 0.6628401319805496 1747 pop,GOP 0.6754436176302232\n",
      "42 0.6776197255666054 1138 pop,GOP 0.3866432337434095\n",
      "103 0.6464299426787143 467 pop,GOP 0.32762312633832974\n",
      "104 0.6915366771458435 908 pop,GOP 0.3656387665198238\n",
      "204 0.6797899642311902 1141 pop,GOP 0.21910604732690622\n",
      "238 0.6780654871596924 876 pop,GOP 0.2420091324200913\n",
      "278 0.6728469236684491 1008 pop,GOP 0.25496031746031744\n",
      "281 0.6562580231050004 199 pop,GOP 0.1306532663316583\n",
      "282 0.673078484015186 1339 pop,GOP 0.3084391336818521\n",
      "287 0.6817486867186682 1149 pop,GOP 0.3185378590078329\n",
      "289 0.6933057992727483 1443 pop,GOP 0.7297297297297297\n",
      "454 0.6967587402750174 8209 pop,GOP 0.5361188938969423\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting underused PRECINCTS\n",
    "for p in range (nPrecincts) :\n",
    "    if precinctUse[p] < 0.7 and isSkippedPrecinct[p] == 0 and vtdPop[p] > 0: #ignore suppressed precincts\n",
    "        print(p,precinctUse[p], vtdPop[p],\"pop,GOP\",vtdGOP[p])\n",
    "        pU = round(precinctUse[p],3)\n",
    "        tractX = vtdGeom[p].centroid.x\n",
    "        tractY = vtdGeom[p].centroid.y        \n",
    "        # print(t, ratio,\"(\",tractX,tractY,\")\",HDvGOP[t], \"t, pop/target,(x,y), pctR\")\n",
    "        plt.text(vtdGeom[p].centroid.x, vtdGeom[p].centroid.y,pU, fontsize=9)\n",
    "        if notPolyVTD[p] == 0 :\n",
    "            x,y = vtdGeom[p].exterior.xy\n",
    "            plt.plot(x,y)\n",
    "        else :\n",
    "            for geom in vtdGeom[p].geoms:\n",
    "                x,y = geom.exterior.xy\n",
    "                plt.plot(x,y)\n",
    "\n",
    "x,y = wholeMAP.exterior.xy\n",
    "plt.plot(x,y,c=\"purple\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 350,
   "id": "37f47a77-d58e-4b4c-be60-676819128e21",
   "metadata": {},
   "outputs": [],
   "source": [
    "# (OPTIONAL) IF WE STOPPED AND RESTARTED, read the data back in:  #NEED TO UPDATE THIS W/ANGLES, RADII\n",
    "import pandas as pd\n",
    "infilename = \"FL_nD28tol0.01nW4.csv\"\n",
    "df2 = pd.read_csv(\"state_HD_output/\"+infilename)\n",
    "HDvPop = df2[\"HD-pop\"]\n",
    "HDvHisp = df2[\"HDvHisp\"]\n",
    "HDvGOP = df2['HDvGOP']\n",
    "HDweight = df2['HDwt']\n",
    "HDarea = df2['HDarea']\n",
    "tractLoopCounter = df2['Loops']\n",
    "tractPop = df2['tractPop']\n",
    "tractCPx = df2['centroid x']\n",
    "tractCPy = df2['centroid y']\n",
    "tractUse = df2['tractUse']\n",
    "# nearEdge = df2['nearEdge']   #add this back in, PLEASE\n",
    "df2.head()\n",
    "nTracts = len(tractPop)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "e9b2c0c0-fb53-44e5-9155-1963d4833ee8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "750596.7418101031 0.1131774380110593 0.0001361943772 0.0400616118781349 0.7450137754374908\n"
     ]
    }
   ],
   "source": [
    "#  HERE'S A BLOCK TO OVERWRITE THE REDONE DATA\n",
    "for r in range(nRedos):\n",
    "    t = redo[r]\n",
    "    HDvPop[t] = redoHDvPop[r]\n",
    "    HDvGOP[t] = redoHDvGOP[r]\n",
    "    tractArea[t] = redoHDarea[r]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "c80e0880-6811-4408-b847-6d70bd2546ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt   #SKIP THIS BLOCK IF STARTING FROM FIRST BLOCK\n",
    "from scipy.stats import norm\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "c8f73bb9-e36b-41c7-9220-1ec09169aa3e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# all done with redo's.  Let's look at some outputs.  First, red/blue by home district  SHOULD BE MIX OF RED AND BLUE\n",
    "nPlot = 5\n",
    "for t in range(nTracts):\n",
    "    if(t % nPlot == 0 and tractPop[t] > minTractPop):\n",
    "        redd = min(max( 0, ( HDvGOP[t] - 0.5) * 3.0 ),1)\n",
    "        bluu = min(max( 0, (0.5 - HDvGOP[t]) * 3.0 ),1)\n",
    "        plt.scatter(tractCPx[t],tractCPy[t],marker='.',color=(redd, 0,bluu ) )\n",
    "x,y = wholeMAP.exterior.xy\n",
    "plt.plot(x,y,color='green')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "a5d3b510-6d1e-49ae-9a5d-03352e2b449e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "map of 18 tracts that were used less than 0.7 of expectation in AZ\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Now, where are those UNDERPERFORMING tracts (<0.x usage)\n",
    "maxPlot = 0.70\n",
    "counter = 0\n",
    "for t in range(nTracts):\n",
    "    if(tractUse[t] < maxPlot and tractUse[t] > 0.05):  #ignore skipped tracts\n",
    "        counter +=1\n",
    "        redd = min(max( 0, ( HDvGOP[t] - 0.5) * 3.0 ),1)\n",
    "        bluu = min(max( 0, (0.5 - HDvGOP[t]) * 3.0 ),1)\n",
    "        plt.scatter(tractCPx[t],tractCPy[t],marker='.',color=(redd, 0,bluu ) )\n",
    "\n",
    "x,y = wholeMAP.exterior.xy   #turn these on if I pull map back in\n",
    "plt.plot(x,y,c=\"green\")\n",
    "print(\"map of {2} tracts that were used less than {0} of expectation in {1}\".format(maxPlot, STATE, counter) )\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "876011e8-d37e-44a6-8dd9-a1e4d2f51e58",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here is a map of tracts that were used more than 1.3 of expectation\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ... And where are the OVERUSED tracts?\n",
    "minPlot = 1.3\n",
    "print(\"here is a map of tracts that were used more than {0} of expectation\".format(minPlot) )\n",
    "for t in range(nTracts):\n",
    "    if(tractUse[t] >minPlot and tractUse[t] > 0.05):  #ignore skipped tracts\n",
    "        redd = min(max( 0, ( HDvGOP[t] - 0.5) * 3.0 ),1)\n",
    "        bluu = min(max( 0, (0.5 - HDvGOP[t]) * 3.0 ),1)\n",
    "        plt.scatter(tractCPx[t],tractCPy[t],marker='.',color=(redd, 0,bluu ) )\n",
    "\n",
    "x,y = wholeMAP.exterior.xy\n",
    "plt.plot(x,y,c=\"green\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "id": "19bfa7c8-0449-4182-8ed9-f5a1c1276f03",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here is a map of 9 tracts that were used more than 1.6 of expectation\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ... And where are the REALLY OVERUSED tracts?\n",
    "minPlot = 1.6\n",
    "counter = 0\n",
    "for t in range(nTracts):\n",
    "    if(tractUse[t] >minPlot and tractUse[t] > 0.05):  #ignore skipped tracts\n",
    "        redd = min(max( 0, ( HDvGOP[t] - 0.5) * 3.0 ),1)\n",
    "        bluu = min(max( 0, (0.5 - HDvGOP[t]) * 3.0 ),1)\n",
    "        plt.scatter(tractCPx[t],tractCPy[t],marker='.',color=(redd, 0,bluu ) )\n",
    "        counter +=1\n",
    "\n",
    "print(\"here is a map of {0} tracts that were used more than {1} of expectation\".format(counter, minPlot) )\n",
    "x,y = wholeMAP.exterior.xy\n",
    "plt.plot(x,y,c=\"green\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "f3e9bde3-06f6-402d-bce1-62b5631e8db0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# CORRELATION OF TRACT UNDER-USAGE IN OTHER TRACT'S HOME DISTRICTS vs. being near a boundary\n",
    "fig, ax = plt.subplots()\n",
    "plt.scatter(tractUse,nearEdge,marker='.' )\n",
    "ax.set(xlabel=\"tract use vs. expectation\", ylabel=\"number of unfilled wedges - max = \"+str(nWedges))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "6bb5c12a-a6a4-4cb1-9026-d227bc11c2ea",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.1490509875323961 = AZ std dev of TRACT usage.  Here is its weighted histogram\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# LET'S VISUALIZE OUR tract usage in a histogram\n",
    "n_bins=50\n",
    "avgTractUse = 0.\n",
    "statePop = np.sum(tractPop)\n",
    "for t in range(nTracts):\n",
    "    avgTractUse += tractUse[t] * tractPop[t]/statePop\n",
    "sumVarTractUse = 0.\n",
    "for t in range(nTracts):\n",
    "    sumVarTractUse += (tractUse[t]-avgTractUse)**2 *tractPop[t]/statePop\n",
    "sdTractUse = sumVarTractUse ** 0.5\n",
    "\n",
    "print(sdTractUse,\"=\",STATE,\"std dev of TRACT usage.  Here is its weighted histogram\")\n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(tractUse, bins=n_bins, weights=tractPop)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "121f9869-3d25-4594-93b3-64a57a95aca8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.15449494846730988 = std dev of PRECINCT usage.  Here is its weighted histogram\n",
      "this is a histogram of PRECINCT usage by precinct for AZ\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# LET'S VISUALIZE OUR precinct usage in a histogram  \n",
    "n_bins=50\n",
    "avgPrecinctUse = 0.\n",
    "stateVTDPop = np.sum(vtdPop)\n",
    "for p in range(nPrecincts):\n",
    "    avgPrecinctUse += precinctUse[p] * vtdPop[p]/stateVTDPop\n",
    "sumVarPrecinctUse = 0.\n",
    "for p in range(nPrecincts):\n",
    "    sumVarPrecinctUse += (precinctUse[p]-avgPrecinctUse)**2 *vtdPop[p]/stateVTDPop\n",
    "sdPrecinctUse = sumVarPrecinctUse ** 0.5\n",
    "\n",
    "print(sdPrecinctUse,\"= std dev of PRECINCT usage.  Here is its weighted histogram\")\n",
    "print(\"this is a histogram of PRECINCT usage by precinct for\",STATE)        \n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(precinctUse, bins=n_bins, weights=vtdPop)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "id": "f6f03dcb-c722-4789-b47f-fb7aa3be97bc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here's a look at numerical stability - number of loops, avg =  9.5333\n"
     ]
    },
    {
     "data": {
      "image/png": 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SUuDh3NMzrD2o9cCBqvpZVf0v8DCwYUifJUkaQcM6B7UKeL3n9RTwx70LJNkMbO5eHkvyygA+9wLg5wPYzu/CUsoK5h2mpZQVzDtMSykr+YeB5f2D2QaHVVCZZax+60XVdmD7QD80mayqiUFuc1iWUlYw7zAtpaxg3mFaSllh+HmHdYhvCljT83o1cGhInyVJGkHDKqgfA2uTXJzk94CNwK4hfZYkaQQN5RBfVb2T5A7gu8AK4KGq2jeMzzrJQA8ZDtlSygrmHaallBXMO0xLKSsMOW+qav6lJEn6HfNmsZKkJllQkqQmjURBLbXbKiU5mOSFJHuTTC52npMleSjJ0SQv9oydn2R3kp92j+ctZsZec+S9J8kb3RzvTXLdYmY8IcmaJD9Isj/JviR3duNNzu8p8jY3v0nen+SZJD/psn6hG291bufK29zcnpBkRZL/SvJ493qoc7vkz0F1t1X6b3puqwTc3PJtlZIcBCaqqsl/kJfkT4BjwL9W1eXd2D8Cb1bVtu5/As6rqr9fzJwnzJH3HuBYVX1xMbOdLMlKYGVVPZfkQ8CzwA3AX9Pg/J4i72dobH6TBDi3qo4lORt4CrgT+EvanNu58l5LY3N7QpK/BSaA36+qTw/798Io7EF5W6UBq6ongTdPGt4A7Oie72Dml1QT5sjbpKo6XFXPdc/fAvYzc+eVJuf3FHmbUzOOdS/P7r6Kdud2rrxNSrIauB74l57hoc7tKBTUbLdVavIHqEcB30vybHfLp6Xgoqo6DDO/tIALFznPQtyR5PnuEGATh3V6JRkHrgCeZgnM70l5ocH57Q5B7QWOArurqum5nSMvNDi3wD8Bfwe82zM21LkdhYKa97ZKDbqyqv4I+HPg9u4QlQbrfuASYB1wGLh3UdOcJMkHgUeAz1bVrxc7z3xmydvk/FbV8apax8zda9YnuXyRI53SHHmbm9sknwaOVtWzv8vPHYWCWnK3VaqqQ93jUeBRZg5Ttu5Idz7ixHmJo4uc55Sq6kj3w/8u8AANzXF3vuER4GtV9a1uuNn5nS1vy/MLUFW/An7IzPmcZuf2hN68jc7tlcBfdOfPHwY+keTfGPLcjkJBLanbKiU5tzvZTJJzgU8BL556rSbsAjZ1zzcBjy1ilnmd+KHp3Egjc9ydGH8Q2F9VX+p5q8n5nStvi/ObZCzJh7vnHwA+CbxMu3M7a94W57aq7qqq1VU1zszv2P+sqr9iyHO75P/k+yLeVulMXQQ8OvNzz1nA16vqO4sb6bcl+QZwFXBBkingbmAbsDPJrcBrwE2Ll/C3zZH3qiTrmDncexC4bbHyneRK4Bbghe7cA8DnaXd+58p7c4PzuxLY0V3Z+z5gZ1U9nuRHtDm3c+X9aoNzO5eh/ne75C8zlySNplE4xCdJGkEWlCSpSRaUJKlJFpQkqUkWlCSpSRaUJKlJFpQkqUn/B+/4r6knTANfAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "avgTLC = 0.\n",
    "usedTracts = 0.\n",
    "for t in range (nTracts):\n",
    "    if(tractLoopCounter[t] > 0):\n",
    "        avgTLC += tractLoopCounter[t]\n",
    "        usedTracts +=1.\n",
    "avgTLC = round(avgTLC / usedTracts, 4)\n",
    "print(\"here's a look at numerical stability - number of loops, avg = \",avgTLC)\n",
    "n_bins=20     \n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(tractLoopCounter, bins=n_bins)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "id": "974631b2-10ef-4847-af2e-a1e26b6fa768",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is a histogram of home-district pct GOP by tract\n",
      "statewide vote is off by -0.00402 0.49441 HD avg vs true 0.49843\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# LET'S VISUALIZE OUR HOME DISTRICT red-blue lean in a histogram  and check the statewide vote vs. HD average\n",
    "n_bins=50\n",
    "print(\"this is a histogram of home-district pct GOP by tract\") \n",
    "print(\"statewide vote is off by\",round((stateGOP2-stateGOP),5),round(stateGOP2,5),\"HD avg vs true\",round(stateGOP,5))\n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(HDvGOP, bins=n_bins,weights=HDweight)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "id": "720d9eb5-11e8-45f7-b402-a0048e9ddb99",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "calculated statewide Hispanic pct was 0.26611, should have been 0.26879\n",
      "this is a histogram of home-district VAP pct Hispanic by tract\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAagAAAEYCAYAAAAJeGK1AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8/fFQqAAAACXBIWXMAAAsTAAALEwEAmpwYAAAV6ElEQVR4nO3df6xf9X3f8eerJkhLkywmXIhlnNlBXjL/ESjzIFq2rBlKh0GqQWokk4owROugxUuQGilWJmXR+o8bkSaNRrGc1BuR2rh0geEJNxR57boqIfMFUcAgNzeuAxcc+4ZUIStSiOG9P77H0Xc3X/ueL77Yn/u9z4d09T3n8+t8zhH45XN8vp+bqkKSpNb8wrmegCRJoxhQkqQmGVCSpCYZUJKkJhlQkqQmnXeuJzCOCy+8sNauXXuupyFJWkSPPPLID6pqan75kgqotWvXMj09fa6nIUlaREm+N6rcR3ySpCYZUJKkJhlQkqQm9QqoJNckOZRkJsn2EfW/nuTx7uebSS5bqG+SC5I8lOQ73efKxTklSdIkWDCgkqwA7gQ2ARuAG5NsmNfsb4F/VVXvAX4b2NWj73Zgf1WtB/Z3+5IkAf3uoK4EZqrqcFW9DOwBNg83qKpvVtXfdbsPA5f06LsZuLvbvhu4/jWfhSRp4vQJqNXAs0P7s13ZqdwK/GmPvhdX1VGA7vOiUYMl2ZpkOsn03Nxcj+lKkiZBn4DKiLKRv6MjyQcYBNSnxu17KlW1q6o2VtXGqamf+x6XJGlC9QmoWWDN0P4lwPPzGyV5D/AVYHNVvdCj77Ekq7q+q4Dj401dkjTJ+gTUAWB9knVJzge2AHuHGyR5B3AvcFNV/U3PvnuBm7vtm4H7X/tpSJImzYJLHVXViSTbgAeBFcDuqjqY5LaufifwGeBtwO8nATjRPZYb2bcbegdwT5JbgWeADy3yuS0pa7c/sGhjHdlx3aKNJUnnSq+1+KpqH7BvXtnOoe3fAH6jb9+u/AXg6nEmK0laPlxJQpLUJANKktQkA0qS1CQDSpLUJANKktQkA0qS1CQDSpLUJANKktQkA0qS1CQDSpLUJANKktQkA0qS1CQDSpLUJANKktQkA0qS1CQDSpLUJANKktQkA0qS1CQDSpLUJANKktQkA0qS1KTz+jRKcg3we8AK4CtVtWNe/buB/wJcAfyHqrqjK38X8MdDTd8JfKaqvpjks8BvAnNd3aerat8ZnEsva7c/sGhjHdlx3aKNJUn6/y0YUElWAHcCHwRmgQNJ9lbVU0PNfgh8HLh+uG9VHQIuHxrnOeC+oSZfOBlmkiQN6/OI70pgpqoOV9XLwB5g83CDqjpeVQeAn55mnKuB71bV917zbCVJy0afgFoNPDu0P9uVjWsL8LV5ZduSPJ5kd5KVozol2ZpkOsn03NzcqCaSpAnUJ6AyoqzGOUiS84FfBf5kqPgu4FIGjwCPAp8f1beqdlXVxqraODU1Nc5hJUlLWJ+AmgXWDO1fAjw/5nE2AY9W1bGTBVV1rKpeqapXgS8zeJQoSRLQL6AOAOuTrOvuhLYAe8c8zo3Me7yXZNXQ7g3Ak2OOKUmaYAu+xVdVJ5JsAx5k8Jr57qo6mOS2rn5nkrcD08BbgFeT3A5sqKoXk7yRwRuAH5039OeSXM7gceGREfWSpGWs1/eguu8n7ZtXtnNo+/sMHv2N6vsS8LYR5TeNNVNJ0rLiShKSpCYZUJKkJhlQkqQmGVCSpCYZUJKkJhlQkqQmGVCSpCYZUJKkJhlQkqQmGVCSpCYZUJKkJhlQkqQmGVCSpCYZUJKkJhlQkqQm9fp9UBpt7fYHzvUUJGlieQclSWqSASVJapIBJUlqkgElSWqSASVJalKvgEpyTZJDSWaSbB9R/+4k30rykySfnFd3JMkTSR5LMj1UfkGSh5J8p/tceeanI0maFAsGVJIVwJ3AJmADcGOSDfOa/RD4OHDHKYb5QFVdXlUbh8q2A/uraj2wv9uXJAnodwd1JTBTVYer6mVgD7B5uEFVHa+qA8BPxzj2ZuDubvtu4Pox+kqSJlyfgFoNPDu0P9uV9VXAnyV5JMnWofKLq+ooQPd50ajOSbYmmU4yPTc3N8ZhJUlLWZ+AyoiyGuMY76uqKxg8IvxYkveP0Zeq2lVVG6tq49TU1DhdJUlLWJ+AmgXWDO1fAjzf9wBV9Xz3eRy4j8EjQ4BjSVYBdJ/H+44pSZp8fQLqALA+ybok5wNbgL19Bk/yi0nefHIb+BXgya56L3Bzt30zcP84E5ckTbYFF4utqhNJtgEPAiuA3VV1MMltXf3OJG8HpoG3AK8muZ3BG38XAvclOXmsP6qqb3RD7wDuSXIr8AzwoUU9M0nSktZrNfOq2gfsm1e2c2j7+wwe/c33InDZKcZ8Abi690wlScuKK0lIkppkQEmSmmRASZKaZEBJkppkQEmSmmRASZKaZEBJkppkQEmSmmRASZKaZEBJkppkQEmSmmRASZKaZEBJkppkQEmSmmRASZKaZEBJkppkQEmSmmRASZKaZEBJkppkQEmSmmRASZKa1CugklyT5FCSmSTbR9S/O8m3kvwkySeHytck+fMkTyc5mOQTQ3WfTfJckse6n2sX55QkSZPgvIUaJFkB3Al8EJgFDiTZW1VPDTX7IfBx4Pp53U8Av1VVjyZ5M/BIkoeG+n6hqu4405OQJE2ePndQVwIzVXW4ql4G9gCbhxtU1fGqOgD8dF750ap6tNv+MfA0sHpRZi5Jmmh9Amo18OzQ/iyvIWSSrAV+Cfj2UPG2JI8n2Z1k5bhjSpImV5+AyoiyGucgSd4EfB24vape7IrvAi4FLgeOAp8/Rd+tSaaTTM/NzY1zWEnSEtYnoGaBNUP7lwDP9z1AkjcwCKc/rKp7T5ZX1bGqeqWqXgW+zOBR4s+pql1VtbGqNk5NTfU9rCRpiVvwJQngALA+yTrgOWAL8OE+gycJ8AfA01X1u/PqVlXV0W73BuDJ3rPWaa3d/sCijXVkx3WLNpYkjWPBgKqqE0m2AQ8CK4DdVXUwyW1d/c4kbwemgbcArya5HdgAvAe4CXgiyWPdkJ+uqn3A55JczuBx4RHgo4t4XpKkJa7PHRRdoOybV7ZzaPv7DB79zfdXjP43LKrqpv7TlCQtN64kIUlqkgElSWqSASVJapIBJUlqkgElSWqSASVJapIBJUlqkgElSWqSASVJapIBJUlqkgElSWqSASVJapIBJUlqkgElSWqSASVJapIBJUlqkgElSWqSASVJapIBJUlqkgElSWqSASVJapIBJUlqUq+ASnJNkkNJZpJsH1H/7iTfSvKTJJ/s0zfJBUkeSvKd7nPlmZ+OJGlSLBhQSVYAdwKbgA3AjUk2zGv2Q+DjwB1j9N0O7K+q9cD+bl+SJKDfHdSVwExVHa6ql4E9wObhBlV1vKoOAD8do+9m4O5u+27g+td2CpKkSdQnoFYDzw7tz3ZlfZyu78VVdRSg+7xo1ABJtiaZTjI9NzfX87CSpKWuT0BlRFn1HP9M+g4aV+2qqo1VtXFqamqcrpKkJaxPQM0Ca4b2LwGe7zn+6foeS7IKoPs83nNMSdIy0CegDgDrk6xLcj6wBdjbc/zT9d0L3Nxt3wzc33/akqRJd95CDarqRJJtwIPACmB3VR1McltXvzPJ24Fp4C3Aq0luBzZU1Yuj+nZD7wDuSXIr8AzwoUU+N0nSErZgQAFU1T5g37yynUPb32fw+K5X3678BeDqcSYrSVo+XElCktQkA0qS1KRej/i0fK3d/sCijXVkx3WLNpakyecdlCSpSQaUJKlJBpQkqUkGlCSpSQaUJKlJBpQkqUkGlCSpSQaUJKlJBpQkqUmuJKGzxlUpJI3DOyhJUpMMKElSkwwoSVKTDChJUpMMKElSkwwoSVKTDChJUpMMKElSk3p9UTfJNcDvASuAr1TVjnn16eqvBV4C/m1VPZrkXcAfDzV9J/CZqvpiks8CvwnMdXWfrqp9Z3IyWj4W60u/fuFXateCAZVkBXAn8EFgFjiQZG9VPTXUbBOwvvu5CrgLuKqqDgGXD43zHHDfUL8vVNUdi3AekqQJ0+cR35XATFUdrqqXgT3A5nltNgNfrYGHgbcmWTWvzdXAd6vqe2c8a0nSxOsTUKuBZ4f2Z7uycdtsAb42r2xbkseT7E6yctTBk2xNMp1kem5ublQTSdIE6hNQGVFW47RJcj7wq8CfDNXfBVzK4BHgUeDzow5eVbuqamNVbZyamuoxXUnSJOgTULPAmqH9S4Dnx2yzCXi0qo6dLKiqY1X1SlW9CnyZwaNESZKAfgF1AFifZF13J7QF2DuvzV7gIxl4L/Cjqjo6VH8j8x7vzfs3qhuAJ8eevSRpYi34Fl9VnUiyDXiQwWvmu6vqYJLbuvqdwD4Gr5jPMHjN/JaT/ZO8kcEbgB+dN/TnklzO4FHgkRH1kqRlrNf3oLrvJ+2bV7ZzaLuAj52i70vA20aU3zTWTCVJy4orSUiSmmRASZKaZEBJkppkQEmSmmRASZKaZEBJkppkQEmSmmRASZKaZEBJkppkQEmSmmRASZKaZEBJkppkQEmSmmRASZKaZEBJkppkQEmSmmRASZKaZEBJkppkQEmSmmRASZKaZEBJkprUK6CSXJPkUJKZJNtH1CfJl7r6x5NcMVR3JMkTSR5LMj1UfkGSh5J8p/tcuTinJEmaBAsGVJIVwJ3AJmADcGOSDfOabQLWdz9bgbvm1X+gqi6vqo1DZduB/VW1Htjf7UuSBPS7g7oSmKmqw1X1MrAH2DyvzWbgqzXwMPDWJKsWGHczcHe3fTdwff9pS5ImXZ+AWg08O7Q/25X1bVPAnyV5JMnWoTYXV9VRgO7zolEHT7I1yXSS6bm5uR7TlSRNgj4BlRFlNUab91XVFQweA34syfvHmB9VtauqNlbVxqmpqXG6SpKWsD4BNQusGdq/BHi+b5uqOvl5HLiPwSNDgGMnHwN2n8fHnbwkaXKd16PNAWB9knXAc8AW4MPz2uwFtiXZA1wF/Kiqjib5ReAXqurH3favAP9pqM/NwI7u8/4zPhtpTGu3P7BoYx3Zcd2ijSWpR0BV1Ykk24AHgRXA7qo6mOS2rn4nsA+4FpgBXgJu6bpfDNyX5OSx/qiqvtHV7QDuSXIr8AzwoUU7K0nSktfnDoqq2scghIbLdg5tF/CxEf0OA5edYswXgKvHmawkaflwJQlJUpMMKElSkwwoSVKTDChJUpMMKElSkwwoSVKTDChJUpMMKElSkwwoSVKTDChJUpMMKElSkwwoSVKTDChJUpMMKElSkwwoSVKTDChJUpMMKElSkwwoSVKTDChJUpMMKElSkwwoSVKTegVUkmuSHEoyk2T7iPok+VJX/3iSK7ryNUn+PMnTSQ4m+cRQn88meS7JY93PtYt3WpKkpe68hRokWQHcCXwQmAUOJNlbVU8NNdsErO9+rgLu6j5PAL9VVY8meTPwSJKHhvp+oaruWLzTkSRNigUDCrgSmKmqwwBJ9gCbgeGA2gx8taoKeDjJW5OsqqqjwFGAqvpxkqeB1fP6ShNh7fYHFm2sIzuuW7SxpKWqzyO+1cCzQ/uzXdlYbZKsBX4J+PZQ8bbukeDuJCv7TlqSNPn6BFRGlNU4bZK8Cfg6cHtVvdgV3wVcClzO4C7r8yMPnmxNMp1kem5ursd0JUmToE9AzQJrhvYvAZ7v2ybJGxiE0x9W1b0nG1TVsap6papeBb7M4FHiz6mqXVW1sao2Tk1N9ZiuJGkS9AmoA8D6JOuSnA9sAfbOa7MX+Ej3Nt97gR9V1dEkAf4AeLqqfne4Q5JVQ7s3AE++5rOQJE2cBV+SqKoTSbYBDwIrgN1VdTDJbV39TmAfcC0wA7wE3NJ1fx9wE/BEkse6sk9X1T7gc0kuZ/Ao8Ajw0UU6J0nSBOjzFh9doOybV7ZzaLuAj43o91eM/vcpquqmsWYqSVpWXElCktQkA0qS1CQDSpLUJANKktQkA0qS1CQDSpLUJANKktQkA0qS1CQDSpLUJANKktQkA0qS1KRea/FJOrv87bySd1CSpEYZUJKkJhlQkqQmGVCSpCYZUJKkJhlQkqQmGVCSpCYZUJKkJhlQkqQmGVCSpCb1Cqgk1yQ5lGQmyfYR9Unypa7+8SRXLNQ3yQVJHkryne5z5eKckiRpEiwYUElWAHcCm4ANwI1JNsxrtglY3/1sBe7q0Xc7sL+q1gP7u31JkoB+i8VeCcxU1WGAJHuAzcBTQ202A1+tqgIeTvLWJKuAtafpuxn45a7/3cBfAJ86w/ORNI8Lz2qp6hNQq4Fnh/Zngat6tFm9QN+Lq+ooQFUdTXLRqIMn2crgrgzg/yY51GPOS9GFwA/O9SSWEK9Xf4t2rfI7izFK8/xvazyLcb3+0ajCPgGVEWXVs02fvqdVVbuAXeP0WYqSTFfVxnM9j6XC69Wf12o8Xq/xvJ7Xq89LErPAmqH9S4Dne7Y5Xd9j3WNAus/j/actSZp0fQLqALA+ybok5wNbgL3z2uwFPtK9zfde4Efd47vT9d0L3Nxt3wzcf4bnIkmaIAs+4quqE0m2AQ8CK4DdVXUwyW1d/U5gH3AtMAO8BNxyur7d0DuAe5LcCjwDfGhRz2zpmfjHmIvM69Wf12o8Xq/xvG7XK4MX7yRJaosrSUiSmmRASZKaZECdRT2WjPr1bqmox5N8M8ll52KerVjoeg21+2dJXknya2dzfq3pc72S/HKSx5IcTPK/zvYcW9Lj/8d/mOR/JPnr7nrdci7m2YIku5McT/LkKepPudzdGakqf87CD4OXRL4LvBM4H/hrYMO8Nv8cWNltbwK+fa7n3fL1Gmr3Pxm8qPNr53reLV8v4K0MVnF5R7d/0bmed+PX69PA73TbU8APgfPP9dzP0fV6P3AF8OQp6q8F/pTBd1/fu1h/dnkHdfb8bMmoqnoZOLns089U1Ter6u+63YcZfG9suVrwenX+PfB1/B5dn+v1YeDeqnoGoKqW8zXrc70KeHOSAG9iEFAnzu4021BVf8ng/E/lZ8vdVdXDwMnl7s6IAXX2nGo5qFO5lcHfSJarBa9XktXADcDOszivVvX57+sfAyuT/EWSR5J85KzNrj19rtd/Bv4Jg8UFngA+UVWvnp3pLTnj/vnWS5+ljrQ4ei/7lOQDDALqX7yuM2pbn+v1ReBTVfXK4C+5y1qf63Ue8E+Bq4F/AHwrycNV9Tev9+Qa1Od6/RvgMeBfA5cCDyX531X14us8t6XojJe1G8WAOnv6LBlFkvcAXwE2VdULZ2luLepzvTYCe7pwuhC4NsmJqvrvZ2WGbem7JNkPqurvgb9P8pfAZcByDKg+1+sWYEcN/pFlJsnfAu8G/s/ZmeKS0uvPt3H5iO/sWXDJqCTvAO4Fblqmf6sdtuD1qqp1VbW2qtYC/w34d8s0nKDfkmT3A/8yyXlJ3sjgNws8fZbn2Yo+1+sZBnebJLkYeBdw+KzOcuk41XJ3Z8Q7qLOk+i0Z9RngbcDvd3cFJ2qZrqrc83qp0+d6VdXTSb4BPA68Cnylqka+Njzpev739dvAf03yBINHWJ+qqmX5aziSfI3B7++7MMks8B+BN8Dpl7s74+N2rwhKktQUH/FJkppkQEmSmmRASZKaZEBJkppkQEmSmmRASZKaZEBJkpr0/wDlhlgrtuL2pgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# LET'S VISUALIZE OUR home district pct Hispanic in a histogram\n",
    "print(\"calculated statewide Hispanic pct was {0}, should have been {1}\"\n",
    "      .format(round(stateHisp2,5), round(np.sum(tractHisp)/np.sum(tractVAP),5) ) )\n",
    "n_bins=50\n",
    "print(\"this is a histogram of home-district VAP pct Hispanic by tract\")        \n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(HDvHisp, bins=[0.1,0.15,0.2,0.25,0.3,0.35,0.4,0.45,0.5,0.55,0.6,0.65,0.7,0.75,0.8,0.85,0.9,0.95,1.0],\n",
    "        weights=HDweight)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "id": "1ab7704f-a226-45a9-98a6-d0684522c1eb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is a bar plot of seats by VAP pct Hispanic for AZ\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ALTERNATE VIEW OF HISPANIC VOTE: HOW MANY DISTRICTS PER 10pct BIN of PCT HISPANIC VOTE?\n",
    "n_bins = 20\n",
    "HispSeats = [0.]*n_bins\n",
    "binMid = [0.]*n_bins\n",
    "for b in range(n_bins):\n",
    "    binMid[b]= float(b)/n_bins + 0.5/n_bins  #centering each bin\n",
    "for t in range(nTracts) :\n",
    "    b = int(HDvHisp[t]*n_bins)\n",
    "    HispSeats[b] += HDweight[t]*nDistricts  #multiply by nDistricts to get number of expected seats\n",
    "\n",
    "print(\"this is a bar plot of seats by VAP pct Hispanic for\",STATE)        \n",
    "fig, ax = plt.subplots()\n",
    "plt.bar(binMid,HispSeats,width=0.09 )\n",
    "#plt.scatter(binMid,HispSeats )\n",
    "ax.set(xlabel=\"pct Hispanic\", ylabel=\"number of seats\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "3ef49161-368a-40a2-83ff-92187edb3462",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "using a threshold of 0.35 the number of Hisp districts should be 2.241\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Where in the state do we see Hispanic-heavy home districts?\n",
    "minHisp1 = 0.35\n",
    "minHisp2 = 0.40\n",
    "sumHispDistricts = 0.\n",
    "for t in range(nTracts):\n",
    "    if (HDvHisp[t] > minHisp1 and tractPop[t] > minTractPop):\n",
    "        sumHispDistricts += tractPop[t]/avgDistrictPop\n",
    "        if (HDvHisp[t] < minHisp2):\n",
    "            plt.scatter(tractCPx[t],tractCPy[t],marker='.',color='orange' )\n",
    "        else :            \n",
    "            plt.text(tractCPx[t],tractCPy[t],'o',color='blue', fontsize= 12)\n",
    "print(\"using a threshold of\",minHisp1,\"the number of Hisp districts should be\",round(sumHispDistricts,3))\n",
    "x,y = origMAP.exterior.xy\n",
    "plt.plot(x,y,c=\"green\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "a92133ad-dc95-4aa1-af02-c1d71296cee6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is a bar plot of seats by VAP pct Black for WI\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ALTERNATE VIEW OF BLACK VOTE: HOW MANY DISTRICTS PER 10pct BIN of PCT BLACK VOTE?\n",
    "n_bins = 10\n",
    "BlackSeats = [0.]*n_bins\n",
    "binMid = [0.]*n_bins\n",
    "for b in range(n_bins):\n",
    "    binMid[b]= float(b)/n_bins + 0.5/n_bins  #centering each bin\n",
    "for t in range(nTracts) :\n",
    "    b = int(HDvBlack[t]*n_bins)\n",
    "    BlackSeats[b] += HDweight[t]*nDistricts  #multiply by nDistricts to get number of expected seats\n",
    "\n",
    "print(\"this is a bar plot of seats by VAP pct Black for\",STATE)        \n",
    "fig, ax = plt.subplots()\n",
    "plt.bar(binMid,BlackSeats,width=0.09 )\n",
    "ax.set(xlabel=\"pct Black\", ylabel=\"number of seats\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "b1e9c437-ddf7-4202-aff0-19f338a5d013",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Black candidates would expect to win 4.701 GA seats out of 14\n",
      "Using simple Alabama-style of Blacks voting 0.9 Dem and whites voting 0.8 GOP\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Where in the state do we see opportunity home districts?  #NEW FORMULATION based on ALABAMA\n",
    "isOppor = [0]*nTracts #will track whether this is an opportunity district\n",
    "blackFracSeats = 0.\n",
    "blackPctDem = 0.9  #fraction of Black voters who are Democrats (est'd from Chen / Stephanopoulos)\n",
    "whitePctGOP = 0.8   #fraction of White voters who are Republican  (\"  \")\n",
    "#minBlack1 = 0.30\n",
    "#minBlack2 = 0.50\n",
    "for t in range(nTracts):\n",
    "    pctWhiteDem = (1-whitePctGOP)*(1-HDvBlack[t])  #this is the fraction of voters who are white Democrats\n",
    "    pctBlackDem = blackPctDem * HDvBlack[t]  #fraction of voters who are black Democrats\n",
    "    if tractPop[t] > minTractPop and HDvGOP[t] < 0.5 :  #Dems would win this Home District\n",
    "        if pctBlackDem > pctWhiteDem : #Blacks would win the primary\n",
    "            isOppor[t] = 1\n",
    "            plt.scatter(tractCPx[t],tractCPy[t],marker='.',color='blue' )\n",
    "            blackFracSeats += HDweight[t]\n",
    "        else :  #the White Dem candidate would win the election          \n",
    "            plt.scatter(tractCPx[t],tractCPy[t],marker='.',color='gray' )\n",
    "\n",
    "print(\"Black candidates would expect to win\",round(blackFracSeats*nDistricts,3),STATE,\"seats out of\",nDistricts)\n",
    "print(\"Using simple Alabama-style of Blacks voting\",blackPctDem,\"Dem and whites voting\",whitePctGOP,\"GOP\")\n",
    "x,y = wholeMAP.exterior.xy\n",
    "plt.plot(x,y,c=\"green\")\n",
    "plt.show()\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "c5125128-e4bb-4337-bc5e-3f48d7cf404f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Here's the GA map with Hisp+Black greater than  0.4 or even 0.5\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Where in the state do we see Black+Hispanic heavy home districts?\n",
    "minMinor = 0.40 \n",
    "minMinor2 = 0.50\n",
    "print(\"Here's the\",STATE,\"map with Hisp+Black greater than \",minMinor,\"or even\",minMinor2)\n",
    "for t in range(nTracts):\n",
    "    if ((HDvBlack[t] + HDvHisp[t]) > minMinor and tractPop[t] > minTractPop):\n",
    "        if (HDvBlack[t] + HDvHisp[t]) > minMinor2 :\n",
    "            plt.scatter(tractCPx[t],tractCPy[t],marker='.',color='blue' )\n",
    "        else :            \n",
    "            plt.scatter(tractCPx[t],tractCPy[t],marker='.',color='gray' )\n",
    "\n",
    "x,y = tractMAP.exterior.xy\n",
    "plt.plot(x,y,c=\"green\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "id": "58119ef8-13ef-4135-9843-6518025dda07",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is a histogram of home-district population by tract; avg =  794611.1111111111\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# LET'S VISUALIZE OUR HOME DISTRICT population in a histogram\n",
    "n_bins=50\n",
    "print(\"this is a histogram of home-district population by tract; avg = \",np.sum(tractPop)/nDistricts)        \n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "a = avgDistrictPop\n",
    "ax.hist(HDvPop, bins=[0.9*a,0.92*a,0.95*a,0.99*a,1.0*a,1.01*a,1.05*a,1.1*a])   #n_bins\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "id": "1f9101bd-108a-48cb-bee5-3f4e04c18132",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is a histogram of home-district area by tract\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# more HOME DISTRICT STATS in a histogram\n",
    "n_bins=50\n",
    "print(\"this is a histogram of home-district area by tract\")        \n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(HDarea, bins=n_bins)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "id": "d8167a80-2b91-4adc-ab34-9e010838213d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# What is the correlation of HD area to red-blue lean?\n",
    "fig, ax = plt.subplots()\n",
    "plt.scatter(HDarea,HDvGOP,marker='.' )\n",
    "ax.set(xlabel=\"tract area\", ylabel=\"home district pct GOP\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "id": "cf95cc7c-e58a-4174-94f6-b6d4c7ccb0ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# What is the correlation of tract usage to red-blue lean?\n",
    "fig, ax = plt.subplots()\n",
    "plt.scatter(HDvGOP,tractUse, marker='.' )\n",
    "ax.set(xlabel=\"home district pct GOP\", ylabel=\"tractUse\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "id": "617384b8-1e0b-45a7-ae09-babe8dd9d429",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# What is the correlation of tract usage to tract population?\n",
    "fig, ax = plt.subplots()\n",
    "plt.scatter(tractPop,tractUse, marker='.' )\n",
    "ax.set(xlabel=\"Census tract population\", ylabel=\"tractUse\")\n",
    "ax.set_xlim(left=0,right=10000)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "id": "91f163ab-6b94-47ee-bd29-be3e751fe2b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# UPDATED VOTES-SEATS CURVE (from votes2seats.ipynb 1/15/22)\n",
    "# LET'S REWORK OUR VOTES - SEATS CALCULATIONS\n",
    "# FIRST, LET'S DO THE BASIC VOTES-TO-SEATS, no fractional seats\n",
    "# INPUTS ARE HDvGOP[nTracts] and HDweight[nTracts]\n",
    "# HDvGOP is the redness lean of each Census tract's Home District\n",
    "# HDweight is the population of this Census tract relative to the state population\n",
    "# stateGOP is the statewide fraction of GOP vote vs. GOP + Dem vote\n",
    "# KEY EQUATION:\n",
    "# expected Seats at a given vote V = sum(HDweight) for tracts with HDvGOP > 0.5 + (V - stateGOP)\n",
    "# for easy calculation, create ~1000 bins, each bin containing HD's in a dV vote window\n",
    "nBins = 1000\n",
    "dV = 1./nBins\n",
    "binWeight = [0.]*nBins\n",
    "\n",
    "for t in range(nTracts):\n",
    "    binNo = int(HDvGOP[t]*nBins)   #which vote bin does this tract's vote go into for the expected statewide vote?\n",
    "    binWeight[binNo] += HDweight[t]\n",
    "    \n",
    "binVote = [0.]*nBins  #this will store the statewide vote for this bin\n",
    "for b in range(nBins) :\n",
    "    binVote[b] = b*dV\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "plt.plot(binVote, binWeight, marker='.',linestyle=\"none\")\n",
    "ax.set(xlabel=\"district pct GOP\", ylabel=\"bin weight\")\n",
    "plt.show()   #this should resemble above histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "id": "85608d3f-c3bd-4d91-9378-16270ceeb9b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#continuing with votes-to-seats, let's compute the S(V) curve with no fractional-seat smearing\n",
    "cumVote = [0.]*nBins\n",
    "for b in range(nBins) :\n",
    "    cumVote[nBins-b-1] = np.sum(binWeight[:(b+1)])  #cumulative weight of all Home Districts below vote b*dV\n",
    "    # print(\"bin, bin vote, cumVote for this bin\",b,b*dV,cumVote[b] )\n",
    "    \n",
    "cumSeats = [0.]*nBins  #this will sum the seats earned for a given statewide vote\n",
    "stateVoteBin = int( (stateGOP+0.5*dV)*nBins )   #which bin (out of 1/dV) holds the statewide total vote?\n",
    "for b in range(nBins) :\n",
    "    bb = int( max(0,min(nBins-1,nBins/2 + b-stateVoteBin)) )\n",
    "    cumSeats[b] = 1. - cumVote[bb]\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "plt.plot(binVote, cumSeats, marker='.',linestyle=\"none\")\n",
    "ax.set(xlabel=\"statewide vote, nBins =\"+str(nBins)+\",\"+str(STATE), ylabel=\"GOP seats won (no fractl smearing)\")\n",
    "RANGE = [0.1, 0.9]\n",
    "fifty50 = [0.5, 0.5]\n",
    "expected = [stateGOP, stateGOP]\n",
    "plt.plot(RANGE,fifty50)\n",
    "plt.plot(fifty50,RANGE)\n",
    "plt.plot(expected,RANGE, linestyle=\"--\",color='red')\n",
    "\n",
    "#LET'S ALSO crudely ESTIMATE RESPONSIVENESS near the STATEWIDE VOTE, with a pseudonormal weighting and lst-sq fit\n",
    "stateSigma = 0.03  #User-adjustable, this is the uncertainty in the statewide vote from election to election\n",
    "usedBins = int(stateSigma/dV)\n",
    "nFitPoints = 6*usedBins\n",
    "voteData = [0.]*nFitPoints\n",
    "seatData = [0.]*nFitPoints\n",
    "counter = 0\n",
    "for b in range(nBins):\n",
    "    if ( abs(b-stateVoteBin) <= 2*usedBins ):  #include this S-V pair in our line fit\n",
    "        voteData[counter]=binVote[b]\n",
    "        seatData[counter]=cumSeats[b]\n",
    "        counter += 1\n",
    "        # print(b,counter)\n",
    "        if ( abs(b-stateVoteBin) < usedBins) : #double count this in data set\n",
    "            voteData[counter]=binVote[b]\n",
    "            seatData[counter]=cumSeats[b]\n",
    "            counter +=1\n",
    "fit = np.polyfit(voteData,seatData,1)  #first-order linear regression with old polyfit\n",
    "Rsimple = fit[0]     #slope is fit[0] in y = mx + b, intercept is fit[1]\n",
    "y0 = fit[1]\n",
    "Rx = [stateGOP - 4.*stateSigma, stateGOP + 4.*stateSigma]\n",
    "Ry = [y0 + Rx[0]*Rsimple, y0 + Rx[1]*Rsimple]\n",
    "plt.plot(Rx,Ry ) \n",
    "ax.text(Rx[1]+0.02, Ry[1]+0.02, \"R = \"+str(round(Rsimple,4)), transform=ax.transAxes, fontsize=14,color='purple')\n",
    "expectedSeats = cumSeats[stateVoteBin]\n",
    "ax.text(0.02,expectedSeats+0.03,\"expected seats = \"+str(round(expectedSeats,3)),transform=ax.transAxes,fontsize=9)\n",
    "ax.text(stateGOP+0.01,0.1,\"HD statewide vote = \"+str(round(stateGOP2,3)),transform=ax.transAxes,fontsize=9)\n",
    "Ex = [0,stateGOP ]\n",
    "Ey = [expectedSeats, expectedSeats]\n",
    "plt.plot(Ex,Ey, linestyle='-.',color='red')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "id": "ce47266f-e2ea-4649-b10c-e8225994c122",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#3/6/22 - alternate votes-to-seats with fractional seat smearing.\n",
    "# First, compute the general cdf sliver for -3 sigma to +3 sigma\n",
    "# Then, apply this smearing to the binVote weights\n",
    "seatVar = 0.04\n",
    "nBins = 1000\n",
    "nSigma = 6.  #how many total sigma of deviation we are explicitly modeling; the rest go into the tails\n",
    "nSlivers = 2*int(3*nBins*seatVar)  #budget for 3 sigma on either side of the expected vote\n",
    "sliverWt = [0.]*nSlivers  #each sliver holds the cdf reflecting the relative distance from the mean vote\n",
    "sliverSigma = [0.]*nSlivers\n",
    "totalSliverWt = 0.\n",
    "for nS in range(nSlivers):\n",
    "    dSigmaHigh = nSigma* (nS+0.5 - nSlivers/2) / float(nSlivers)\n",
    "    dSigmaLow =  nSigma* (nS-0.5 - nSlivers/2) / float(nSlivers)\n",
    "    sliverSigma[nS] = 0.5*(dSigmaHigh+dSigmaLow)  #for plotting; keeps track of this sliver's center sigma\n",
    "    sliverWt[nS] = norm.cdf(dSigmaHigh) - norm.cdf(dSigmaLow)\n",
    "    totalSliverWt += sliverWt[nS]\n",
    "lowSliverWt = 0.5 * (1. - totalSliverWt)\n",
    "highSliverWt = lowSliverWt\n",
    "#print(\"each tail of distribution has weight\",round(lowSliverWt,4) )\n",
    "#plt.plot(sliverSigma,sliverWt)\n",
    "#plt.show()\n",
    "# OK, that worked as expected.  Now, do the smearing of each bin\n",
    "smearedBinWeight = [0.]*nBins\n",
    "for b in range(nBins):\n",
    "    centerBinNo = b #int(HDvGOP[t]*nBins)   #which vote bin does this tract's vote go into for the expected statewide vote?\n",
    "    binOffset = int(nSlivers/2)\n",
    "    for nS in range(nSlivers):\n",
    "        binNo = centerBinNo + nS - binOffset\n",
    "        if (binNo < 0): #deep blue district; variance would push vote %R < 0\n",
    "            binNo = 0\n",
    "        if (binNo >= nBins):  #deep red district; variance would push vote %R > 1\n",
    "            binNo = nBins - 1\n",
    "        smearedBinWeight[binNo] += binWeight[b]*sliverWt[nS]\n",
    "    # Now put the tails into the correct bins\n",
    "    binNo = centerBinNo -1 - binOffset  #left tail\n",
    "    if binNo < 0:\n",
    "        binNo = 0\n",
    "    smearedBinWeight[binNo] += binWeight[b]*lowSliverWt\n",
    "    binNo = centerBinNo + nSlivers - binOffset  #right tail\n",
    "    if binNo >= nBins:\n",
    "        binNo = nBins -1 \n",
    "    smearedBinWeight[binNo] += binWeight[b]*highSliverWt\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "plt.plot(binVote, binWeight, marker='.',linestyle=\"none\",label=\"unsmeared\")\n",
    "plt.plot(binVote, smearedBinWeight, marker='o',linestyle=\"none\",label=\"smeared\")\n",
    "RANGE = [0.0, 0.5*np.max(binWeight)]\n",
    "sGOP = [stateGOP, stateGOP]\n",
    "plt.plot(sGOP,RANGE,linestyle=\"-\",label=\"statewide \"+str(round(stateGOP,3)) )\n",
    "ax.set(xlabel=\"district pct GOP\", ylabel=\"bin weight\")\n",
    "plt.legend()\n",
    "plt.show()   #this should resemble above histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "id": "8a2d7de3-ea93-4447-bb69-36defbae38df",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#continuing with votes-to-seats, let's compute the S(V) curve with no fractional-seat smearing\n",
    "smearedBinVote = [0.]*nBins  #this will store the statewide vote for this bin\n",
    "cumSmearedVote = [0.]*nBins\n",
    "for b in range(nBins) :\n",
    "    smearedBinVote[b] = b*dV\n",
    "    cumSmearedVote[nBins-b-1] = np.sum(smearedBinWeight[:(b+1)])  #cumulative weight of all Home Districts below vote b*dV\n",
    "    # print(\"bin, bin vote, cumVote for this bin\",b,b*dV,cumVote[b] )\n",
    "    \n",
    "cumSmearedSeats = [0.]*nBins  #this will sum the seats earned for a given statewide vote\n",
    "stateVoteBin = int( (stateGOP+0.5*dV)*nBins )   #which bin (out of 1/dV) holds the statewide total vote?\n",
    "for b in range(nBins) :\n",
    "    bb = int( max(0,min(nBins-1,nBins/2 + b-stateVoteBin)) )\n",
    "    cumSmearedSeats[b] = 1. - cumSmearedVote[bb]\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "plt.plot(smearedBinVote,cumSmearedSeats, marker='o',linestyle=\"none\",label=str(seatVar)+\" smear\")\n",
    "plt.plot(binVote, cumSeats, marker='.',linestyle=\"none\",label=\"no smear\")\n",
    "ax.set(xlabel=\"true \"+STATE+\"-wide vote=\"+str(round(stateGOP,4))+\", nBins =\"+str(nBins), ylabel=\"GOP seats won\")\n",
    "RANGE = [0.1, 0.9]\n",
    "fifty50 = [0.5, 0.5]\n",
    "expected = [stateGOP, stateGOP]\n",
    "expectedSeats = cumSeats[stateVoteBin]\n",
    "expectedS = [expectedSeats,expectedSeats]\n",
    "smearedSeats = cumSmearedSeats[stateVoteBin]\n",
    "smearedS = [smearedSeats,smearedSeats]\n",
    "plt.plot(RANGE,fifty50)\n",
    "plt.plot(fifty50,RANGE)\n",
    "plt.plot(RANGE,smearedS, linestyle=\"--\",color='blue',label=str(round(expectedSeats,3))+\"no smear\")\n",
    "plt.plot(RANGE,expectedS, linestyle=\"--\",color='orange',label=str(round(smearedSeats,3))+\"smeared\")\n",
    "plt.plot(expected,RANGE, linestyle=\"--\",color='red')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "id": "597b7758-4f83-40f7-8909-4946e64267ad",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "simpler and fractional-seat-smeared (var of 0.04 ) responsiveness are 3.172 3.231\n",
      "fractional expected GOP seats =  4.542  out of  9 seats for AZ\n",
      "simpler expected GOP seats =  4.5222  out of  9 seats\n"
     ]
    }
   ],
   "source": [
    "#Finally, let's compare responsiveness using smeared (fractional seat variance) to non-smeared calculated above\n",
    "#LET'S ALSO crudely ESTIMATE (smeared) RESPONSIVENESS near the STATEWIDE VOTE, as we did for non-smeared\n",
    "stateSigma = 0.03  #User-adjustable, this is the uncertainty in the statewide vote from election to election\n",
    "usedBins = int(stateSigma/dV)\n",
    "nFitPoints = 6*usedBins\n",
    "voteData = [0.]*nFitPoints\n",
    "seatData = [0.]*nFitPoints\n",
    "counter = 0\n",
    "for b in range(nBins):\n",
    "    if ( abs(b-stateVoteBin) <= 2*usedBins ):  #include this S-V pair in our line fit\n",
    "        voteData[counter]=smearedBinVote[b]\n",
    "        seatData[counter]=cumSmearedSeats[b]\n",
    "        counter += 1\n",
    "        # print(b,counter)\n",
    "        if ( abs(b-stateVoteBin) < usedBins) : #double count this in data set\n",
    "            voteData[counter]=smearedBinVote[b]\n",
    "            seatData[counter]=cumSmearedSeats[b]\n",
    "            counter +=1\n",
    "fit = np.polyfit(voteData,seatData,1)  #first-order linear regression with old polyfit\n",
    "Rsmeared = fit[0]     #slope is fit[0] in y = mx + b, intercept is fit[1]\n",
    "y0 = fit[1]\n",
    "\n",
    "print(\"simpler and fractional-seat-smeared (var of\",seatVar,\") responsiveness are\",round(Rsimple,3) ,round(Rsmeared,3) )   \n",
    "print(\"fractional expected GOP seats = \",round(nDistricts*smearedSeats,3),\" out of \",nDistricts,\"seats for\",STATE)\n",
    "print(\"simpler expected GOP seats = \",round(nDistricts*expectedSeats,4),\" out of \",nDistricts,\"seats\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "86f82727-7193-4194-aa61-2b99408dbcb6",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "kernelspec": {
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   "language": "python",
   "name": "python3"
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  "language_info": {
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    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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   "pygments_lexer": "ipython3",
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